Academic literature on the topic 'Nearest neighboring methods'
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Journal articles on the topic "Nearest neighboring methods"
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Hou, Z., Y. Chen, K. Tan, and P. Du. "NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 539–46. http://dx.doi.org/10.5194/isprs-archives-xlii-3-539-2018.
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Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.
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Sironen, S., A. Kangas, M. Maltamo, and J. Kangas. "Estimating individual tree growth with nonparametric methods." Canadian Journal of Forest Research 33, no. 3 (March 1, 2003): 444–49. http://dx.doi.org/10.1139/x02-162.
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The aim of the study was to demonstrate the use of nonparametric methods in estimating tree-level growth models. In the nonparametric methods the growth of a tree is predicted as a weighted mean of the values of neighboring observations. The selection of the nearest neighbors is based on the similarities between tree- and stand-level characteristics of the target tree and the neighbors. The data for the models were collected from Kuusamo in northeastern Finland. Models for the 5-year diameter growth were constructed for Scots pine (Pinus sylvestris L.) with three different nonparametric methods: the k-nearest neighbor regression, k-most-similar neighbor, and generalized additive model.
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Maltamo, Matti, and Annika Kangas. "Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution." Canadian Journal of Forest Research 28, no. 8 (August 1, 1998): 1107–15. http://dx.doi.org/10.1139/x98-085.
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In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.
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Cui, Liang Yu, Wei Liu, Yan Chun Xu, Shu Hui Yang, and Thomas D. Dahmer. "A new method to estimate hair density of small mammal pelage." Journal of Mammalogy 101, no. 4 (May 16, 2020): 1205–12. http://dx.doi.org/10.1093/jmammal/gyaa048.
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Abstract Hair density is the most important structural parameter contributing to insulation performance of mammalian pelage, and often is measured in ecophysiological, thermal biological, and evolutionary studies. To date, hair density has been measured using invasive methods on research objects; however, such methods remain challenging despite efforts to increase their ease of use. In this paper, we develop a new method to estimate hair density without skin sampling. We expressed hair density as the inverse of the number of hairs per unit area, that is, the surface area occupied by a single hair (Ah). This area could be further estimated by measuring distances between nearest neighboring hairs (Ln) and calculating the areas of triangles (A) defined by three randomly selected nearest neighboring hairs and representing half of Ah. Empirical tests using 11 skin samples from specimens of six small mammal species showed this to be a simple, lightly invasive, but accurate and widely applicable method.
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Cariou, Claude, Steven Le Moan, and Kacem Chehdi. "Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 22 (November 14, 2020): 3745. http://dx.doi.org/10.3390/rs12223745.
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We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images.
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Mao, Jia Li, Hong Ying Jin, Ming Dong Li, and Jia Li. "Ml-KNN Algorithm Based on Frequent Item Sets." Applied Mechanics and Materials 380-384 (August 2013): 1533–37. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1533.
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In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors ( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.
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Wang, Hongxinag, Gongqiao Zhang, Gangying Hui, Yuanfa Li, Yanbo Hu, and Zhonghua Zhao. "The influence of sampling unit size and spatial arrangement patterns on neighborhood-based spatial structure analyses of forest stands." Forest Systems 25, no. 1 (April 1, 2016): 056. http://dx.doi.org/10.5424/fs/2016251-07968.
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Aim of the study: Neighborhood-based stand spatial structure parameters can quantify and characterize forest spatial structure effectively. How these neighborhood-based structure parameters are influenced by the selection of different numbers of nearest-neighbor trees is unclear, and there is some disagreement in the literature regarding the appropriate number of nearest-neighbor trees to sample around reference trees. Understanding how to efficiently characterize forest structure is critical for forest management.Area of study: Multi-species uneven-aged forests of Northern ChinaMaterial and methods: We simulated stands with different spatial structural characteristics and systematically compared their structure parameters when two to eight neighboring trees were selected.Main results: Results showed that values of uniform angle index calculated in the same stand were different with different sizes of structure unit. When tree species and sizes were completely randomly interspersed, different numbers of neighbors had little influence on mingling and dominance indices. Changes of mingling or dominance indices caused by different numbers of neighbors occurred when the tree species or size classes were not randomly interspersed and their changing characteristics can be detected according to the spatial arrangement patterns of tree species and sizes.Research highlights: The number of neighboring trees selected for analyzing stand spatial structure parameters should be fixed. We proposed that the four-tree structure unit is the best compromise between sampling accuracy and costs for practical forest management.
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Amin, Faris Muslihul. "Identifikasi Citra Daging Ayam Berformalin Menggunakan Metode Fitur Tekstur dan K-Nearest Neighbor (K-NN)." Jurnal Matematika "MANTIK" 4, no. 1 (May 30, 2018): 68–74. http://dx.doi.org/10.15642/mantik.2018.4.1.68-74.
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The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.
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Zheng, Min, Malgorzata Biczysko, Yanting Xu, Nigel W. Moriarty, Holger Kruse, Alexandre Urzhumtsev, Mark P. Waller, and Pavel V. Afonine. "Including crystallographic symmetry in quantum-based refinement: Q|R#2." Acta Crystallographica Section D Structural Biology 76, no. 1 (January 1, 2020): 41–50. http://dx.doi.org/10.1107/s2059798319015122.
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Three-dimensional structure models refined using low-resolution data from crystallographic or electron cryo-microscopy experiments can benefit from high-quality restraints derived from quantum-chemical methods. However, nonperiodic atom-centered quantum-chemistry codes do not inherently account for nearest-neighbor interactions of crystallographic symmetry-related copies in a satisfactory way. Here, these nearest-neighbor effects have been included in the model by expanding to a super-cell and then truncating the super-cell to only include residues from neighboring cells that are interacting with the asymmetric unit. In this way, the fragmentation approach can adequately and efficiently include nearest-neighbor effects. It has previously been shown that a moderately sized X-ray structure can be treated using quantum methods if a fragmentation approach is applied. In this study, a target protein (PDB entry 4gif) was partitioned into a number of large fragments. The use of large fragments (typically hundreds of atoms) is tractable when a GPU-based package such as TeraChem is employed or cheaper (semi-empirical) methods are used. The QM calculations were run at the HF-D3/6-31G level. The models refined using a recently developed semi-empirical method (GFN2-xTB) were compared and contrasted. To validate the refinement procedure for a non-P1 structure, a standard set of crystallographic metrics were used. The robustness of the implementation is shown by refining 13 additional protein models across multiple space groups and a summary of the refinement metrics is presented.
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Karimzadeh, Sadra, Masashi Matsuoka, Jianming Kuang, and Linlin Ge. "Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective." ISPRS International Journal of Geo-Information 8, no. 10 (October 22, 2019): 462. http://dx.doi.org/10.3390/ijgi8100462.
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Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.
Dissertations / Theses on the topic "Nearest neighboring methods"
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Deng, Hai. "Identifying Calcium-Binding Sites and Predicting Disulfide Connectivity." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/21.
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Most questions in proteomics require complex answers. Yet graph theory, supervised learning, and statistical model have decomposed complex questions into simple questions with simple answers. The expertise in the field of protein study often address tasks that demand answers as complex as the questions. Such complex answers may consist of multiple factors that must be weighed against each other to arrive at a globally satisfactory and consistent solution to the question. In the prediction of calcium binding in proteins, we construct a global oxygen contact graph of a protein, then apply a graph algorithm to find oxygen clusters with the fixed size of four, finally employ a geometry algorithm to judge if the oxygen clusters are calcium-binding sites or not. Additionally, we can predict the locations of those sites. Furthermore, we construct a global oxygen contact graph including oxygen-bonded carbon atoms of a protein, then apply a graph algorithm to find local biggest oxygen clusters, finally design another geometric filter to exclude the non-calcium binding oxygen clusters. In addition, we apply observed chemical properties as a chemical filter to recognize some non-calcium binding oxygen clusters. In order to explore the characteristics of calcium-binding sites in proteins, we conduct a statistic survey on four datasets derived from 1994 to 2005 about the geometric parameters and chemical properties of calcium-binding sites. In the prediction of disulfide bond connectivity, we analyze protein sequences to predict the folding of proteins relative to the cystines using nearest neighboring methods. we extend a new pattern-wise method to all available template proteins, and find global pattern of pairing cysteines with a new descriptor of cysteine separation profile on protein secondary structure.
Book chapters on the topic "Nearest neighboring methods"
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Kumar, Rajesh, and Rajeev Srivastava. "Detection of Cancer from Microscopic Biopsy Images Using Image Processing Tools." In Research Developments in Computer Vision and Image Processing, 175–94. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4558-5.ch010.
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Presently, most cancer diagnosis is based on human visual examination of images in a qualitative manner. Human visual grading for microscopic biopsy images is very time-consuming, subjective, and inconsistent due to inter-and intra-observer variations. A more quantitative and reproducible approach for analyzing biopsy images is highly desired. In biopsy images, the characteristics of nuclei are the key to estimate the degree of malignancy. The microscopic biopsy images always suffer from the problem of impurities, undesirable elements, and uneven exposure. Thus, there is a need of an automatic cancer diagnosis system based on microscopic biopsy images using image-processing tools. Therefore, the cancer and its type will be detected in a very early stage for complete treatment and cure. This system helps pathologists to improve the accuracy and efficiency in detection of malignancy and to minimize the inter observer variation. In addition, the method may help physicians to analyze the image cell by using classification and clustering algorithms by staining characteristics of the cells. The various image-processing steps involved for cancer detection from biopsy images include acquisition, enhancement, segmentation, feature extraction, image representation, classification, and decision-making. With the help of image, processing tools the sizes of cells, nuclei, and cytoplasm as well as the mean distance between two nearest neighboring nuclei are estimated by the system.
Conference papers on the topic "Nearest neighboring methods"
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Wang, Ke, and Xin Geng. "Binary Coding based Label Distribution Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/386.
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Label Distribution Learning (LDL) is a novel learning paradigm in machine learning, which assumes that an instance is labeled by a distribution over all labels, rather than labeled by a logic label or some logic labels. Thus, LDL can model the description degree of all possible labels to an instance. Although many LDL methods have been put forward to deal with different application tasks, most existing methods suffer from the scalability issue. In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL. The proposed framework includes two parts, i.e., binary coding and label distribution generation. In the binary coding part, the learning objective is to generate the optimal binary codes for the instances. We integrate the label distribution information of the instances into a binary coding procedure, leading to high-quality binary codes. In the label distribution generation part, given an instance, the k nearest training instances in the Hamming space are searched and the mean of the label distributions of all the neighboring instances is calculated as the predicted label distribution. Experiments on five benchmark datasets validate the superiority of BC-LDL over several state-of-the-art LDL methods.
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Xu, Q. Y., W. M. Feng, and B. C. Liu. "3D Stochastic Modeling of As-Cast Microstructure for Aluminum Alloy Casting." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32894.
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A 3D stochastic modeling was carried out to simulate the dendritic grains during solidification process of aluminum alloy, including time-dependent calculations for temperature field, solute redistribution in liquid, curvature of the dendritic tip, and growth anisotropy. The nucleation process was calculated by continuous nucleation. A 3D simplified grain shape model was established to represent the equiaxed dendritic grain. Based on the Cellular Automaton method, a grain growth model was proposed to capture the neighbor cells of the nucleated cell. On growing, each grain continues to capture the nearest neighbor cells to form the final shape. When a neighboring cell has been captured by the other grains, the grain growth along this direction is stopped. Three-dimensional calculations were performed to simulate the evolution of dendritic grain. In order to verify the modeling results, aluminum alloy sample castings were cast in sand and metal mold.