The Experts below are selected from a list of 11166 Experts worldwide ranked by ideXlab platform
Amir Averbuch - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Bandwidth selection for manifold learning and classification
Data Mining and Knowledge Discovery, 2020Co-Authors: Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir AverbuchAbstract:Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s Bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold’s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.
Ofir Lindenbaum - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Bandwidth selection for manifold learning and classification
Data Mining and Knowledge Discovery, 2020Co-Authors: Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir AverbuchAbstract:Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s Bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold’s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.
Moshe Salhov - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Bandwidth selection for manifold learning and classification
Data Mining and Knowledge Discovery, 2020Co-Authors: Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir AverbuchAbstract:Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s Bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold’s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.
Arie Yeredor - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Bandwidth selection for manifold learning and classification
Data Mining and Knowledge Discovery, 2020Co-Authors: Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir AverbuchAbstract:Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s Bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold’s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.
Qiao Hong - One of the best experts on this subject based on the ideXlab platform.
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A novel CBIR system with WLLTSA and ULRGA
Neurocomputing, 2015Co-Authors: Lin Feng, Shenglan Liu, Yao Xiao, Qiao HongAbstract:Abstract At present, relevance feedback (RF) has been widely applied in content-based image retrieval (CBIR) system. Local Regression and Global Alignment (LRGA) is a novel ranking algorithm used in CBIR system which utilizes RF technique. However, there are some problems in LRGA: (1) for handling the problem of out-of-sample, dimension reduction is used after RF, but it is time-consuming; (2) feature space of images is often assumed to be linear. While, classical manifold learning methods are sensitive to the Gaussian Bandwidth parameter of Laplacian matrix and cannot be combined with RF either. To address problems above, this paper proposes a novel CBIR system. Firstly, we calculate the local curvature parameter of manifold utilizing the angle information in subspace to avoid local high curvature problem and then we propose a Warp Linear Local Tangent Space Alignment (WLLTSA) algorithm; furthermore, we propose a U-Local Regression and Global Alignment (ULRGA) ranking algorithm to rank low-dimensional image features. Curvature parameter is used in both WLLTSA and ULRGA to enhance robustness. A large amount of experimental results demonstrate the efficiency of our CBIR system.