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Academic literature on the topic 'Sparse features'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Sparse features"

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Caragea, Cornelia, Adrian Silvescu, and Prasenjit Mitra. "Combining Hashing and Abstraction in Sparse High Dimensional Feature Spaces." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 3–9. http://dx.doi.org/10.1609/aaai.v26i1.8117.

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With the exponential increase in the number of documents available online, e.g., news articles, weblogs, scientific documents, the development of effective and efficient classification methods is needed. The performance of document classifiers critically depends, among other things, on the choice of the feature representation. The commonly used "bag of words" and n-gram representations can result in prohibitively high dimensional input spaces. Data mining algorithms applied to these input spaces may be intractable due to the large number of dimensions. Thus, dimensionality reduction algorithms that can process data into features fast at runtime, ideally in constant time per feature, are greatly needed in high throughput applications, where the number of features and data points can be in the order of millions. One promising line of research to dimensionality reduction is feature clustering. We propose to combine two types of feature clustering, namely hashing and abstraction based on hierarchical agglomerative clustering, in order to take advantage of the strengths of both techniques. Experimental results on two text data sets show that the combined approach uses significantly smaller number of features and gives similar performance when compared with the "bag of words" and n-gram approaches.

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Simion, Georgiana. "Sparse Features for Finger Detection." Advanced Engineering Forum 8-9 (June 2013): 535–42. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.535.

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The use of hand as direct input device evolved with the development of Natural User Interfaces (NUI). Touch screens are well integrated in our daily life and the new challenge is to implement interfaces which involve no direct contact. This paper presents such a solution implemented within the framework of sparse techniques. The feature vectors representing key distances and angles are extracted and used to detect fingers. The experimental results have demonstrated that this technique is able to obtain an error rate about 5% in finger detection.

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Kronvall, Ted, Maria Juhlin, Johan Swärd, Stefan I. Adalbjörnsson, and Andreas Jakobsson. "Sparse modeling of chroma features." Signal Processing 130 (January 2017): 105–17. http://dx.doi.org/10.1016/j.sigpro.2016.06.020.

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He, Wangpeng, Peipei Zhang, Xuan Liu, Binqiang Chen, and Baolong Guo. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis." Sustainability 14, no. 24 (2022): 16793. http://dx.doi.org/10.3390/su142416793.

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Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases.

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Banihashem, Kiarash, Mohammad Hajiaghayi, and Max Springer. "Optimal Sparse Recovery with Decision Stumps." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6745–52. http://dx.doi.org/10.1609/aaai.v37i6.25827.

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Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. We examine the statistical properties of these "decision stumps" for the recovery of the s active features from p total features, where s

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Xing, Zhan, Jianhui Lin, Yan Huang, and Cai Yi. "A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering." Shock and Vibration 2020 (July 25, 2020): 1–20. http://dx.doi.org/10.1155/2020/2019821.

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The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

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Wei, Wang, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle, and Li Ji. "Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation." Computational Intelligence and Neuroscience 2019 (November 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/8258275.

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An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.

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YANG, B. J. "DOMINANT EIGENVECTOR AND EIGENVALUE ALGORITHM IN SPARSE NETWORK SPECTRAL CLUSTERING." Latin American Applied Research - An international journal 48, no. 4 (2018): 323–28. http://dx.doi.org/10.52292/j.laar.2018.248.

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The sparse network spectrum clustering problem is studied in this paper. It tries to analyze and improve the sparse network spectrum clustering algorithm from the main feature pair algorithm. The main feature pair algorithm in the matrix calculation is combined with the spectral clustering algorithm to explore the application of the main feature pair algorithm on the network adjacency matrix. The defects of traditional main features are analyzed when the algorithm Power is used on the network of special structural features, and the advantages of the new algorithm SII algorithm is proved. The sparse network spectral clustering algorithm in this paper is based on the Score algorithm, and the main features of the algorithm are refined, analyzed and improved.

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SUN, JUN, WENYUAN WANG, QING ZHUO, and CHENGYUAN MA. "DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION." International Journal of Image and Graphics 03, no. 03 (2003): 503–21. http://dx.doi.org/10.1142/s0219467803001135.

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Feature extraction is very important in the subject of pattern recognition. Sparse coding is an approach for extracting the independent features of an image. The image features extracted by sparse coding have led to better recognition performance as compared to those from traditional PCA-based methods. A new discriminatory sparse coding (DSC) algorithm is proposed in this paper to further improve the classification performance. Based on reinforcement learning, DSC encodes the training samples by individual class rather than by individual image as in sparse coding. Having done that it will produce a set of features with large and small intraclass variations, which is very suitable for recognition tasks. Experiments are performed on face image feature extraction and recognition. Compared with the traditional PCA- and ICA-based methods, DSC shows a much better recognition performance.

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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 1 (2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.

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Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.

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Dissertations / Theses on the topic "Sparse features"

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Strohmann, Thomas. "Very sparse kernel models: Predicting with few examples and few features." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239405.

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Radwan, Noha [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Leveraging sparse and dense features for reliable state estimation in urban environments." Freiburg : Universität, 2019. http://d-nb.info/1190031361/34.

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Hata, Alberto Yukinobu. "Road features detection and sparse map-based vehicle localization in urban environments." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08062017-090428/.

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Localization is one of the fundamental components of autonomous vehicles by enabling tasks as overtaking, lane keeping and self-navigation. Urban canyons and bad weather interfere with the reception of GPS satellite signal which prohibits the exclusive use of such technology for vehicle localization in urban places. Alternatively, map-aided localization methods have been employed to enable position estimation without the dependence on GPS devices. In this solution, the vehicle position is given as the place that best matches the sensor measurement to the environment map. Before building the maps, feature sof the environment must be extracted from sensor measurements. In vehicle localization, curbs and road markings have been extensively employed as mapping features. However, most of the urban mapping methods rely on a street free of obstacles or require repetitive measurements of the same place to avoid occlusions. The construction of an accurate representation of the environment is necessary for a proper match of sensor measurements to the map during localization. To prevent the necessity of a manual process to remove occluding obstacles and unobserved areas, a vehicle localization method that supports maps built from partial observations of the environment is proposed. In this localization system,maps are formed by curb and road markings extracted from multilayer laser sensor measurements. Curb structures are detected even in the presence of vehicles that occlude the roadsides, thanks to the use of robust regression. Road markings detector employs Otsu thresholding to analyze infrared remittance data which makes the method insensitive to illumination. Detected road features are stored in two map representations: occupancy grid map (OGM) and Gaussian process occupancy map (GPOM). The first approach is a popular map structure that represents the environment through fine-grained grids. The second approach is a continuous representation that can estimate the occupancy of unseen areas. The Monte Carlo localization (MCL) method was adapted to support the obtained maps of the urban environment. In this sense, vehicle localization was tested in an MCL that supports OGM and an MCL that supports GPOM. Precisely, for MCL based on GPOM, a new measurement likelihood based on multivariate normal probability density function is formulated. Experiments were performed in real urban environments. Maps were built using sparse laser data to verify there ronstruction of non-observed areas. The localization system was evaluated by comparing the results with a high precision GPS device. Results were also compared with localization based on OGM.<br>No contexto de veículos autônomos, a localização é um dos componentes fundamentais, pois possibilita tarefas como ultrapassagem, direção assistida e navegação autônoma. A presença de edifícios e o mau tempo interferem na recepção do sinal de GPS que consequentemente dificulta o uso de tal tecnologia para a localização de veículos dentro das cidades. Alternativamente, a localização com suporte aos mapas vem sendo empregada para estimar a posição sem a dependência do GPS. Nesta solução, a posição do veículo é dada pela região em que ocorre a melhor correspondência entre o mapa do ambiente e a leitura do sensor. Antes da criação dos mapas, características dos ambientes devem ser extraídas a partir das leituras dos sensores. Dessa forma, guias e sinalizações horizontais têm sido largamente utilizados para o mapeamento. Entretanto, métodos de mapeamento urbano geralmente necessitam de repetidas leituras do mesmo lugar para compensar as oclusões. A construção de representações precisas dos ambientes é essencial para uma adequada associação dos dados dos sensores como mapa durante a localização. De forma a evitar a necessidade de um processo manual para remover obstáculos que causam oclusão e áreas não observadas, propõe-se um método de localização de veículos com suporte aos mapas construídos a partir de observações parciais do ambiente. No sistema de localização proposto, os mapas são construídos a partir de guias e sinalizações horizontais extraídas a partir de leituras de um sensor multicamadas. As guias podem ser detectadas mesmo na presença de veículos que obstruem a percepção das ruas, por meio do uso de regressão robusta. Na detecção de sinalizações horizontais é empregado o método de limiarização por Otsu que analisa dados de reflexão infravermelho, o que torna o método insensível à variação de luminosidade. Dois tipos de mapas são empregados para a representação das guias e das sinalizações horizontais: mapa de grade de ocupação (OGM) e mapa de ocupação por processo Gaussiano (GPOM). O OGM é uma estrutura que representa o ambiente por meio de uma grade reticulada. OGPOM é uma representação contínua que possibilita a estimação de áreas não observadas. O método de localização por Monte Carlo (MCL) foi adaptado para suportar os mapas construídos. Dessa forma, a localização de veículos foi testada em MCL com suporte ao OGM e MCL com suporte ao GPOM. No caso do MCL baseado em GPOM, um novo modelo de verossimilhança baseado em função densidade probabilidade de distribuição multi-normal é proposto. Experimentos foram realizados em ambientes urbanos reais. Mapas do ambiente foram gerados a partir de dados de laser esparsos de forma a verificar a reconstrução de áreas não observadas. O sistema de localização foi avaliado por meio da comparação das posições estimadas comum GPS de alta precisão. Comparou-se também o MCL baseado em OGM com o MCL baseado em GPOM, de forma a verificar qual abordagem apresenta melhores resultados.

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Pundlik, Shrinivas J. "Motion segmentation from clustering of sparse point features using spatially constrained mixture models." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937182/.

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Quadros, Alistair James. "Representing 3D shape in sparse range images for urban object classification." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10515.

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This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing.

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Mairal, Julien. "Sparse coding for machine learning, image processing and computer vision." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595312.

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We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.

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Abbasnejad, Iman. "Learning spatio-temporal features for efficient event detection." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121184/1/Iman_Abbasnejad_Thesis.pdf.

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This thesis has addressed the topic of event detection in videos, which is a challenging problem as events to be detected, can be complex, correlated, and may require the detection of different objects and human actions. To address these challenges, the thesis has developed effective strategies for learning the spatio-temporal features of events. Improved event detection performance has been demonstrated on several real-world challenging databases. The outcome of our research will be useful for a number of applications including human computer interaction, robotics and video surveillance.

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Lakemond, Ruan. "Multiple camera management using wide baseline matching." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37668/1/Ruan_Lakemond_Thesis.pdf.

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Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera networks, cameras are placed far apart to maximise coverage. This is referred to as a wide base-line scenario. Finding sufficient correspondences for camera calibration in wide base-line scenarios presents a significant challenge. This thesis focuses on developing more effective and efficient techniques for finding correspondences in uncalibrated, wide baseline, multiple-camera scenarios. The project consists of two major areas of work. The first is the development of more effective and efficient view covariant local feature extractors. The second area involves finding methods to extract scene information using the information contained in a limited set of matched affine features. Several novel affine adaptation techniques for salient features have been developed. A method is presented for efficiently computing the discrete scale space primal sketch of local image features. A scale selection method was implemented that makes use of the primal sketch. The primal sketch-based scale selection method has several advantages over the existing methods. It allows greater freedom in how the scale space is sampled, enables more accurate scale selection, is more effective at combining different functions for spatial position and scale selection, and leads to greater computational efficiency. Existing affine adaptation methods make use of the second moment matrix to estimate the local affine shape of local image features. In this thesis, it is shown that the Hessian matrix can be used in a similar way to estimate local feature shape. The Hessian matrix is effective for estimating the shape of blob-like structures, but is less effective for corner structures. It is simpler to compute than the second moment matrix, leading to a significant reduction in computational cost. A wide baseline dense correspondence extraction system, called WiDense, is presented in this thesis. It allows the extraction of large numbers of additional accurate correspondences, given only a few initial putative correspondences. It consists of the following algorithms: An affine region alignment algorithm that ensures accurate alignment between matched features; A method for extracting more matches in the vicinity of a matched pair of affine features, using the alignment information contained in the match; An algorithm for extracting large numbers of highly accurate point correspondences from an aligned pair of feature regions. Experiments show that the correspondences generated by the WiDense system improves the success rate of computing the epipolar geometry of very widely separated views. This new method is successful in many cases where the features produced by the best wide baseline matching algorithms are insufficient for computing the scene geometry.

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Umakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.

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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

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Dhanjal, Charanpal. "Sparse Kernel feature extraction." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.

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The presence of irrelevant features in training data is a significant obstacle for many machine learning tasks, since it can decrease accuracy, make it harder to understand the learned model and increase computational and memory requirements. One approach to this problem is to extract appropriate features. General approaches such as Principal Components Analysis (PCA) are successful for a variety of applications, however they can be improved upon by targeting feature extraction towards more specific problems. More recent work has been more focused and considers sparser formulations which potentially have improved generalisation. However, sparsity is not always efficiently implemented and frequently requires complex optimisation routines. Furthermore, one often does not have a direct control on the sparsity of the solution. In this thesis, we address some of these problems, first by proposing a general framework for feature extraction which possesses a number of useful properties. The framework is based on Partial Least Squares (PLS), and one can choose a user defined criterion to compute projection directions. It draws together a number of existing results and provides additional insights into several popular feature extraction methods. More specific feature extraction is considered for three objectives: matrix approximation, supervised feature extraction and learning the semantics of two-viewed data. Computational and memory efficiency is prioritised, as well as sparsity in a direct manner and simple implementations. For the matrix approximation case, an analysis of different orthogonalisation methods is presented in terms of the optimal choice of projection direction. The analysis results in a new derivation for Kernel Feature Analysis (KFA) and the formation of two novel matrix approximation methods based on PLS. In the supervised case, we apply the general feature extraction framework to derive two new methods based on maximising covariance and alignment respectively. Finally, we outline a novel sparse variant of Kernel Canonical Correlation Analysis (KCCA) which approximates a cardinality constrained optimisation. This method, as well as a variant which performs feature selection in one view, is applied to an enzyme function prediction case study.

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Books on the topic "Sparse features"

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Flexible Sparse Learning of Feature Subspaces. [publisher not identified], 2017.

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DiPietro, Vincent. Unusual Mars surface features. 4th ed. Mars Research, 1988.

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Schommers, W. Cosmic secrets: Basic features of reality. World Scientific, 2012.

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Davis, James E. Environmental satellites: Features and acquisition plans. Edited by Thompson Gregory F and United States. General Accounting Office. Nova Novinka, 2012.

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Eliseeva, Elena. Khudozhestvennoe prostranstvo v otechestvennykh igrovykh filʹmakh XX veka. "Starklaĭt", 2012.

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1965-, Carlson Laura Anne, and Zee Emile van der, eds. Functional features in language and space: Insights from perception, categorization, and development. Oxford University Press, 2005.

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J, Müller Hermann, and Deutsche Forschungsgemeinschaft, eds. Neural binding of space and time: Spatial and temporal mechanisms of feature-object binding. Psychology Press, 2001.

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Crompton, John L. The proximate principle: The impact of parks, open space and water features on residential property values and the property tax base. 2nd ed. National Recreation and Park Association, 2004.

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Doorn, Niels van. Digital spaces, material traces: Investigating the performance of gender, sexuality, and embodiment on internet platforms that feature user-generated content. [s.n.], 2009.

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Dynamic feature space modelling, filtering, and self-tuning control of stochastic systems: A systems approach with economic and social applications. Springer-Verlag, 1985.

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Book chapters on the topic "Sparse features"

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Ranzato, Marc’Aurelio, Y.-Lan Boureau, Koray Kavukcuoglu, Karol Gregor, and Yann LeCun. "Learning Hierarchies of Sparse Features." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1880.

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Zou, Yuan, and Teemu Roos. "Sparse Logistic Regression with Logical Features." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31753-3_26.

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Haker, Martin, Thomas Martinetz, and Erhardt Barth. "Multimodal Sparse Features for Object Detection." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_93.

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Carneiro, Gustavo, and David Lowe. "Sparse Flexible Models of Local Features." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_3.

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Ujaldon, M., E. L. Zapata, B. M. Chapman, and H. P. Zima. "Data-parallel Language Features for Sparse Codes." In Languages, Compilers and Run-Time Systems for Scalable Computers. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2315-4_19.

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Rebecchi, Sébastien, Hélène Paugam-Moisy, and Michèle Sebag. "Learning Sparse Features with an Auto-Associator." In Growing Adaptive Machines. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55337-0_4.

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Barata, Catarina, Mário A. T. Figueiredo, M. Emre Celebi, and Jorge S. Marques. "Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_58.

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Gaur, Yashesh, Maulik C. Madhavi, and Hemant A. Patil. "Speaker Recognition Using Sparse Representation via Superimposed Features." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_19.

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Zhang, Ziming, Jiawei Huang, and Ze-Nian Li. "Learning Sparse Features On-Line for Image Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21593-3_13.

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Du, Jianhao, Weihua Sheng, Qi Cheng, and Meiqin Liu. "Proactive 3D Robot Mapping in Environments with Sparse Features." In Advances in Visual Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_74.

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Conference papers on the topic "Sparse features"

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Metwally, Ahmed, and Michael Shum. "Similarity Joins of Sparse Features." In SIGMOD/PODS '24: International Conference on Management of Data. ACM, 2024. http://dx.doi.org/10.1145/3626246.3653370.

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Chang, Jen-Hao Rick, Aswin C. Sankaranarayanan, and B. V. K. Vijaya Kumar. "Random Features for Sparse Signal Classification." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.583.

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Ge, Tiezheng, Qifa Ke, and Jian Sun. "Sparse-Coded Features for Image Retrieval." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.132.

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Iglesias, Gonzalo, Adrià de Gispert, and Bill Byrne. "Transducer Disambiguation with Sparse Topological Features." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1273.

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Chakrabarti, Ayan, and Keigo Hirakawa. "Effective separation of sparse and non-sparse image features for denoising." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517745.

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Yin, Chong, Siqi Liu, Vincent Wai-Sun Wong, and Pong C. Yuen. "Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/220.

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Liver biopsy images play a key role in the diagnosis of global non-alcoholic fatty liver disease (NAFLD). The NAFLD activity score (NAS) on liver biopsy images grades the amount of histological findings that reflect the progression of NAFLD. However, liver biopsy image analysis remains a challenging task due to its complex tissue structures and sparse distribution of histological findings. In this paper, we propose a sparse interpretable feature learning method (SparseX) to efficiently estimate NAS scores. First, we introduce an interpretable spatial sampling strategy based on histological features to effectively select informative tissue regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability. regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability.

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Zhang, Xiaowang, Qiang Gao, and Zhiyong Feng. "InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/602.

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In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction. To characterize multilevel interaction of features, InteractionNN consists of three modules, namely, nonlinear interaction pooling, layer-lossing, and embedding. Nonlinear interaction pooling (NI pooling) is a hierarchical structure and, by shortcut connection, constructs low-level feature interactions from basic dense features to elementary features. Layer-lossing is a feed-forward neural network where high-level feature interactions can be learned from low-level feature interactions via correlation of all layers with target. Moreover, embedding is to extract basic dense features from sparse features of data which can help in reducing our proposed model computational complex. Finally, our experiment evaluates on the two benchmark datasets and the experimental results show that InteractionNN performs better than most of state-of-the-art models in sparse regression.

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Kaarna, A. "Sparse Coded Spatial Features from Spectral Images." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.949.

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Ozcelikkale, Ayca. "Sparse Recovery with Non-Linear Fourier Features." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054050.

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Sainath, Tara N., David Nahamoo, Bhuvana Ramabhadran, Dimitri Kanevsky, Vaibhava Goel, and Parikshit M. Shah. "Exemplar-based Sparse Representation phone identification features." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947352.

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Reports on the topic "Sparse features"

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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity.

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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.

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Naikal, Nikhil, Allen Yang, and S. S. Sastry. Informative Feature Selection for Object Recognition via Sparse PCA. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada543168.

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Rainey, Katie, and Ana Ascencio. Sparse Representation and Dictionary Learning as Feature Extraction in Vessel Imagery. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613963.

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Horvath, Ildiko. Investigating repeatable ionospheric features during large space storms and superstorms. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609369.

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Veth, Mike, and Meir Pachter. Correspondence Search Mitigation Using Feature Space Anti-Aliasing. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada473005.

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Bonnie, David John, and Kyle E. Lamb. MarFS: A Scalable Near-POSIX Name Space over Cloud Objects – New Features. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1333128.

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Méndez-Vizcaíno, Juan C., and Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, 2021. http://dx.doi.org/10.32468/be.1175.

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Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-Switching process for public transfers with an explosive regime. The most notable additions to the existing literature for Latin America are the estimations of entire distributions of public debt limits for various default probabilities and that said limits critically hinge on both current and future states. Results obtained indicate notorious contractions of Fiscal Spaces among all countries during the pandemic, but the sizes of these were very heterogeneous. Countries that in 2019 had positive spaces and got closer to negative spaces in 2020, have since seen deterioration of their sovereign debt ratings or outlooks. Colombia was the only country to lose its positive Fiscal Space and investment grade, thereby joining Brazil, the previously sole member of both groups

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Slotiuk, Tetiana. CONCEPT OF SOLUTIONS JOURNALISM MODEL: CONNOTION, FUNCTIONS, FEATURES OF FUNCTIONING. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11097.

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The article examines the main features, general characteristics and essence of the concept of solutions journalism. The basic principles of functioning of this model of journalism in the western press and in Ukraine are given. The list and features of activity of the organizations, institutes and editorial offices supporting development of journalism of solutions journalism. The purpose of the publication is to describe the Solutions Journalism model: its features, characteristics and features of functioning, to find out the difference in the understanding of the concept of «solutions journalism» and «constructive journalism» in general. The task of the publication was to conceptualize the main trends in the development of solutions journalism in the Western and Ukrainian information space; show the main characteristics, formats of functioning and analyze the features of the concepts of «solutions journalism» and «constructive journalism». Applied research methods: at the stage of research of the history of formation of the concept of Solutions Journalism the historical method is used. The hermeneutic method of research helped in the interpretation of basic concepts, the phenomenological approach was applied in the context of considering the essence of the phenomenon of solutions journalism. At the stage of generalization of the features of the concepts of Solutions Journalism and «constructive journalism» a comparative method was used, which gave an understanding of the common components in their essence. The method of analysis allowed to expand the understanding of the purpose of Solutions Journalism as a type of social journalism and its main tasks. With the help of synthesis it was possible to comprehensively understand the concept of Solutions Journalism and understand its features. In Ukraine, this type of journalism is just emerging, but its introduction into the editorial policy of the media may have a national importance. These are regional and local media that can inform their communities about the positive solution of certain problems in other communities, and thus thanks to this model can save local journalism. In the scientific context, there is a need to outline the main differences in the understanding of the concepts of decision journalism and constructive journalism, to understand the socio-psychological need to create good news.

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Jackiewicz, Jason. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada563097.

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