Deep subspace learning for expression recognition driven by a two-phase representation classifier
Sign Up to like & getrecommendations! Published in 2020 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-019-01568-4
Abstract: Recent research has shown that the deep subspace learning (DSL) method can extract high-level features and better represent abstract semantics of data for facial expression recognition. While significant advances have been made in this area,… read more here.
Keywords: deep subspace; expression recognition; subspace learning; representation ... See more keywords
Non-intrusive Sparse Subspace Learning for Parametrized Problems
Sign Up to like & getrecommendations! Published in 2019 at "Archives of Computational Methods in Engineering"
DOI: 10.1007/s11831-017-9241-4
Abstract: We discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive… read more here.
Keywords: non intrusive; subspace learning; sparse subspace; subspace ... See more keywords
Subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation
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DOI: 10.1016/j.neucom.2020.06.111
Abstract: Abstract Traditional unsupervised feature selection methods usually construct a fixed similarity matrix. This matrix is sensitive to noise and becomes unreliable, which affects the performance of feature selection. The researches have shown that both the… read more here.
Keywords: subspace learning; feature; feature selection; structure ... See more keywords
Tensor low-rank sparse representation for tensor subspace learning
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DOI: 10.1016/j.neucom.2021.02.002
Abstract: Abstract Many subspace learning methods are implemented on a matrix of sample data. For multi-dimensional data, these methods have to convert data samples into vectors in advance, which often destroys the inherent spatial structure of… read more here.
Keywords: rank sparse; tensor; subspace learning; low rank ... See more keywords
Trace Ratio Criterion Based Large Margin Subspace Learning for Feature Selection
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DOI: 10.1109/access.2018.2888924
Abstract: In this paper, we propose a novel feature selection model based on subspace learning with the use of a large margin principle. First, we present a new margin metric described by a given instance and… read more here.
Keywords: margin; ratio; feature selection; subspace learning ... See more keywords
A General Approach for Achieving Supervised Subspace Learning in Sparse Representation
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DOI: 10.1109/access.2019.2898923
Abstract: Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are… read more here.
Keywords: supervised subspace; approach; subspace learning; subspace ... See more keywords
Low-Rank Subspace Learning of Multikernel Based on Weighted Truncated Nuclear Norm for Image Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3183901
Abstract: Previous natural image segmentation algorithms through subspace learning method have over-segmentation issues in the pre-segmentation process, which will compromise the edge information, and the subspace learning model cannot effectively utilize the nonlinear structure in the… read more here.
Keywords: norm image; subspace learning; image segmentation; segmentation ... See more keywords
Fast Adaptive Local Subspace Learning With Regressive Regularization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3193631
Abstract: Linear Discriminant Analysis (LDA) has been widely used in supervised dimensionality reduction fields. However, LDA is usually weak in tackling data with Non-Gaussian distribution due to its incapability of extracting the intrinsic structure of data.… read more here.
Keywords: local subspace; adaptive local; subspace learning; regularization ... See more keywords
Generalized Robust Regression for Jointly Sparse Subspace Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2018.2812802
Abstract: Ridge regression is widely used in multiple variable data analysis. However, in very high-dimensional cases such as image feature extraction and recognition, conventional ridge regression or its extensions have the small-class problem, that is, the… read more here.
Keywords: regression; robust regression; generalized robust; subspace learning ... See more keywords
Scalable Structured Compressive Video Sampling With Hierarchical Subspace Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2019.2939370
Abstract: Conventional scalable compressive video sampling cannot sufficiently exploit the structured sparsity within video sequences. This paper proposes a novel quality scalable structured compressive video sampling (SS-CVS) framework with hierarchical subspace learning to support video transmission… read more here.
Keywords: video; video sampling; hierarchical subspace; subspace learning ... See more keywords
Partial Tubal Nuclear Norm-Regularized Multiview Subspace Learning.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2023.3263175
Abstract: In this article, a unified multiview subspace learning model, called partial tubal nuclear norm-regularized multiview subspace learning (PTN 2 MSL), was proposed for unsupervised multiview subspace clustering (MVSC), semisupervised MVSC, and multiview dimension reduction. Unlike… read more here.
Keywords: subspace; multiview subspace; subspace learning; nuclear norm ... See more keywords