Готові списки джерел за темами / Locality preserving projection

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Добірка наукової літератури з теми "Locality preserving projection"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Locality preserving projection"

Wong, W. K., and H. T. Zhao. "Supervised optimal locality preserving projection." Pattern Recognition 45, no. 1 (January 2012): 186–97. http://dx.doi.org/10.1016/j.patcog.2011.05.014.

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Yin, Jun, and Shiliang Sun. "Multiview Uncorrelated Locality Preserving Projection." IEEE Transactions on Neural Networks and Learning Systems 31, no. 9 (September 2020): 3442–55. http://dx.doi.org/10.1109/tnnls.2019.2944664.

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Xu, Yong, Binglei Xie, and Jingyu Yang. "Theoretical analysis of locality preserving projection and a fast orthogonal locality preserving projection algorithm." Journal of Electronic Imaging 21, no. 3 (September 14, 2012): 033024–1. http://dx.doi.org/10.1117/1.jei.21.3.033024.

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Zhang, Qi Rong, and Zhong Shi He. "Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition." Advanced Materials Research 121-122 (June 2010): 391–98. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.391.

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Анотація:

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

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Sun, Chuang, Zhousuo Zhang, Zhengjia He, Zhongjie Shen, Binqiang Chen, and Wenrong Xiao. "Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 3 (April 24, 2013): 548–60. http://dx.doi.org/10.1177/0954406213486735.

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Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

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Li, Jun-Bao, Jeng-Shyang Pan, and Shu-Chuan Chu. "Kernel class-wise locality preserving projection." Information Sciences 178, no. 7 (April 2008): 1825–35. http://dx.doi.org/10.1016/j.ins.2007.12.001.

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Guo Jinyu, 郭金玉, and 苑玮琦 Yuan Weiqi. "Palmprint Recognition Based on Locality Preserving Projection." Acta Optica Sinica 28, no. 10 (2008): 1920–24. http://dx.doi.org/10.3788/aos20082810.1920.

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Liang, Chunyan, Wei Cao, and Shuxin Cao. "Locality Preserving Discriminant Projection for Speaker Verification." Journal of Computer and Communications 08, no. 11 (2020): 14–22. http://dx.doi.org/10.4236/jcc.2020.811002.

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Long, Tianhang, Yanfeng Sun, Junbin Gao, Yongli Hu, and Baocai Yin. "Locality preserving projection based on Euler representation." Journal of Visual Communication and Image Representation 70 (July 2020): 102796. http://dx.doi.org/10.1016/j.jvcir.2020.102796.

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Shikkenawis, Gitam, and Suman K. Mitra. "On some variants of locality preserving projection." Neurocomputing 173 (January 2016): 196–211. http://dx.doi.org/10.1016/j.neucom.2015.01.100.

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Більше джерел

Дисертації з теми "Locality preserving projection"

Wu, Che-Ming, and 吳哲明. "A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87139611163568193184.

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Анотація:

碩士
淡江大學
資訊工程學系碩士班
98
Due to cost consideration, the quality of images captured from surveillance systems usually is poor. It makes the face recognition difficult in these low-resolution images. Here we propose a block-based algorithm called Orthogonal Locality Preserving Projections (OLPP) for super-resolution of face images. The purpose is to discover the local structure of the manifold and produce orthogonal basis functions for face images. To train the system, we divide the high-resolution images and the corresponding low-resolution images into 4 blocks (forehead, eyes, nose, and mouth). For each block, we use the low-resolution ones to find an OLPP transformation matrix. Then, use the obtained coefficients from the OLPP (input) and the corresponding high-resolution one (target) to train a GRNN (General Regression Neural Network). For an unseen low-resolution face image, it is divided into 4 blocks similarly and the corresponding coefficients for each block are obtained by the trained OLPP transformation matrix. Finally, an improved super-resolution block is obtained by feeding the coefficients of OLPP into GRNN. And a super-resolution face image is achieved by combining all blocks. Comparing to existing methods, the proposed method has shown an improved and promising results.

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Chu, Kun-Long, and 褚坤龍. "Face Detection and Face Recognition Based on Gabor Wavelets and Locality Preserving Projection." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/evjyc7.

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Анотація:

碩士
國立交通大學
電機學院電機產業專班
96
Face recognition has been a popular research topic for a long time because it can be widely used in many different fields, such as identity identification, content-based image retrieval, computer vision and human computer interaction. However, face detection, which serves as the preprocessing procedure, is equally important since it has to be done first before face recognition is taken. This thesis, therefore, proposes a method which takes the advantages of Gabor transformation and locality preserving projection to implement face recognition and face detection on a digital picture. The first step adopts face detection to find candidate face region. Second, Gabor wavelets transformation is adopted to extract face features of human face, and locality preserving projection is applied to project features of human face into lower dimension space. Afterwards, neural network is trained to decide whether candidate region is human face or not. Then, database is constructed manually according to the result of face detection. Finally, a neural network is trained by the faces is stored in the database. When a test picture is input the proposed method is able to identify the faces of the chosen people. According to the result of identification, pictures of the same person can be chosen from database and implement the identify-based image retrieval. The main contribution of this thesis is to employ the specialty of Gabor wavelets transformation, which is to maintain sufficient recognition rate in both time domain and frequency domain, to obtain face feature; moreover apply the strength of locality preserving projection, which preserves the local structure of the multidimensional structure, the immense feature vectors of Gabor wavelets transformation is lowered to minimum. The experiment results show the proposed method has good performance in both face detection and face recognition.

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Liu, Jun-Zuo, and 劉俊佐. "Improved Facial Expression Recognition System Based on Symmetric Features and New Locality Preserving Projection." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/20435046153450765910.

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Анотація:

碩士
國立臺灣大學
電信工程學研究所
100
Based on the increasing of accessible data and the fast development of the computational technology, machine learning attracted lots of attention in the last ten year because of the great demand of automation in human life. Now in the disciplines of pattern recognition, robotics, artificial intelligences, computer vision, and even economics, machine learning has been an indispensible part to extract and discover the valuable information from data. On the other hand, human face related topics such as face detection and recognition became important research fields in pattern recognition and computer vision during the last few decades. This is due to the needs of automatic recognition and surveillance system, the interest in the human visual system on human face perception, and the design of human-computer interface, etc. In this thesis, we focus on using machine learning techniques for facial expression recognition. A facial expression recognition framework is proposed, which includes four steps: feature extraction, denoising mechanism, dimensionality reduction, and facial expression determination. The widely-used local binary pattern feature (LBP) is modified and combined with a new feature extraction method, local phase quantization (LPQ) to represent the facial expression. Since the extracted features are noisy and contain unrelated information for expression recognition task, a denoising mechanism is proposed. Due to the denoising mechanism, the denoised features are more representative for facial expression. Different from the existing dimensionality reduction algorithms, an expression-specific dimensionality reduction algorithm is proposed based on the special properties of facial expression. Finally, the reduced features with more meaning for facial expression are fed into the widely-used Support Vector Machine (SVM) and K-nearest neighbor classifier. From the experimental results, the proposed framework and algorithms achieve the highest recognition rate against the existing methods based on the JAFFE database.

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Teng, Luyao. "Research on Joint Sparse Representation Learning Approaches." Thesis, 2019. https://vuir.vu.edu.au/40024/.

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Анотація:

Dimensionality reduction techniques such as feature extraction and feature selection are critical tools employed in artificial intelligence, machine learning and pattern recognitions tasks. Previous studies of dimensionality reduction have three common problems: 1) The conventional techniques are disturbed by noise data. In the context of determining useful features, the noises may have adverse effects on the result. Given that noises are inevitable, it is essential for dimensionality reduction techniques to be robust from noises. 2) The conventional techniques separate the graph learning system apart from informative feature determination. These techniques used to construct a data structure graph first, and keep the graph unchanged to process the feature extraction or feature selection. Hence, the result of feature extraction or feature selection is strongly relying on the graph constructed. 3) The conventional techniques determine data intrinsic structure with less systematic and partial analyzation. They maintain either the data global structure or the data local manifold structure. As a result, it becomes difficult for one technique to achieve great performance in different datasets. We propose three learning models that overcome prementioned problems for various tasks under different learning environment. Specifically, our research outcomes are listing as followings: 1) We propose a novel learning model that joints Sparse Representation (SR) and Locality Preserving Projection (LPP), named Joint Sparse Representation and Locality Preserving Projection for Feature Extraction (JSRLPP), to extract informative features in the context of unsupervised learning environment. JSRLPP processes the feature extraction and data structure learning simultaneously, and is able to capture both the data global and local structure. The sparse matrix in the model operates directly to deal with different types of noises. We conduct comprehensive experiments and confirm that the proposed learning model performs impressive over the state-of-the-art approaches. 2) We propose a novel learning model that joints SR and Data Residual Relationships (DRR), named Unsupervised Feature Selection with Adaptive Residual Preserving (UFSARP), to select informative features in the context of unsupervised learning environment. Such model does not only reduce disturbance of different types of noise, but also effectively enforces similar samples to have similar reconstruction residuals. Besides, the model carries graph construction and feature determination simultaneously. Experimental results show that the proposed framework improves the effect of feature selection. 3) We propose a novel learning model that joints SR and Low-rank Representation (LRR), named Sparse Representation based Classifier with Low-rank Constraint (SRCLC), to extract informative features in the context of supervised learning environment. When processing the model, the Low-rank Constraint (LRC) regularizes both the within-class structure and between-class structure while the sparse matrix works to handle noises and irrelevant features. With extensive experiments, we confirm that SRLRC achieves impressive improvement over other approaches. To sum up, with the purpose of obtaining appropriate feature subset, we propose three novel learning models in the context of supervised learning and unsupervised learning to complete the tasks of feature extraction and feature selection respectively. Comprehensive experimental results on public databases demonstrate that our models are performing superior over the state-of-the-art approaches.

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Частини книг з теми "Locality preserving projection"

Bhatt, Pranjal, Sujata, and Suman K. Mitra. "Kernel Variants of Extended Locality Preserving Projection." In Communications in Computer and Information Science, 130–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4018-9_12.

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Shikkenawis, Gitam, and Suman K. Mitra. "A New Proposal for Locality Preserving Projection." In Perception and Machine Intelligence, 298–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27387-2_37.

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Hu, Dewen, and Ling-Li Zeng. "Locality Preserving Projection of Functional Connectivity for Regression." In Pattern Analysis of the Human Connectome, 123–47. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9523-0_7.

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Shikkenawis, Gitam, Suman K. Mitra, and Ajit Rajwade. "A New Orthogonalization of Locality Preserving Projection and Applications." In Lecture Notes in Computer Science, 277–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_38.

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Huo, Guang, Qi Zhang, Huan Guo, Wenyu Li, and Yangrui Zhang. "Multi-source Heterogeneous Iris Recognition Using Locality Preserving Projection." In Biometric Recognition, 304–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31456-9_34.

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Zheng, Zhonglong, Jianmin Zhao, and Jie Yang. "Gabor Feature Based Face Recognition Using Supervised Locality Preserving Projection." In Advanced Concepts for Intelligent Vision Systems, 644–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864349_59.

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Wang, Xianliang, Jinchao Yang, Chunyan Liang, Ruohua Zhou, and Yonghong Yan. "Locality Preserving Discriminant Projection for Total-Variability-Based Language Recognition." In Advances in Intelligent Systems and Computing, 451–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37835-5_39.

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Lu, Chong, Xiaodong Liu, and Wanquan Liu. "Face Recognition via Two Dimensional Locality Preserving Projection in Frequency Domain." In Lecture Notes in Computer Science, 271–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15615-1_33.

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Kumar, Santosh, Sanjay Kumar Singh, Rishav Singh, and Amit Kumar Singh. "Real-Time Recognition of Cattle Using Fisher Locality Preserving Projection Method." In Animal Biometrics, 197–221. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7956-6_7.

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Jin, Xin, Yi Liu, Jie Ren, Anbang Xu, and Rongfang Bie. "Locality Preserving Projection on Source Code Metrics for Improved Software Maintainability." In Lecture Notes in Computer Science, 877–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_92.

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Тези доповідей конференцій з теми "Locality preserving projection"

Shikkenawis, Gitam, and Suman K. Mitra. "Locality Preserving Discriminant Projection." In IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). IEEE, 2015. http://dx.doi.org/10.1109/isba.2015.7126365.

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Zhao, Haitao, and Shaoyuan Sun. "Optimal Locality Preserving Projection." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5653271.

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Sun, Shaoyuan, Haitao Zhao, and Huijun Yang. "Discriminant uncorrelated locality preserving projection." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647191.

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Chen, Zipei. "Adaptively Discriminant Locality Preserving Projection." In 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 2021. http://dx.doi.org/10.1109/icaice54393.2021.00119.

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Kezheng Lin, Shu Li, and Jingtian Li. "Multiple information projection based on Locality Preserving Projections." In 2013 IEEE Conference Anthology. IEEE, 2013. http://dx.doi.org/10.1109/anthology.2013.6784809.

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Zheng, Zhonglong, and Jianmin Zhao. "Locality Preserving Projection in Orthogonal Domain." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.71.

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Shikkenawis, Gitam, and Suman K. Mitra. "Kernelization of locality preserving discriminant projection." In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2015. http://dx.doi.org/10.1109/ncvpripg.2015.7489993.

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Sun, Shaoyuan, and Haitao Zhao. "Normalized Laplacian based Optimal Locality Preserving Projection." In 2010 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2010. http://dx.doi.org/10.1109/icalip.2010.5684530.

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Long, Tianhang, Junbin Gao, Mingyan Yang, Yongli Hu, and Baocai Yin. "Locality Preserving Projection via Deep Neural Network." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852218.

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Song, Xin, Xinwei Jiang, Junbin Gao, Zhihua Cai, and Xia Hong. "Functional Locality Preserving Projection for Dimensionality Reduction." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489598.

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