Entropy-based active sparse subspace clustering
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DOI: 10.1007/s11042-018-5945-1
Abstract: Sparse Subspace Clustering (SSC) is widely used in data mining and machine learning. Some studies have been developed to add pairwise constraints as side information to improve the clustering results. However, most of these algorithms… read more here.
Keywords: pairwise constraints; entropy based; sparse subspace; subspace clustering ... See more keywords
Multi-geometric Sparse Subspace Clustering
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DOI: 10.1007/s11063-020-10274-z
Abstract: Recently, the Riemannian manifold has received special attention in unsupervised clustering since the real-world visual data usually resides on a special manifold where Euclidean geometry fails to capture. Although many clustering algorithms have been proposed,… read more here.
Keywords: subspace clustering; multi geometric; geometry; sparse subspace ... See more keywords
Subspace Learning by $$\ell ^{0}$$ℓ0-Induced Sparsity
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DOI: 10.1007/s11263-018-1092-4
Abstract: Subspace clustering methods partition the data that lie in or close to a union of subspaces in accordance with the subspace structure. Such methods with sparsity prior, such as sparse subspace clustering (SSC) (Elhamifar and… read more here.
Keywords: ell ssc; clustering methods; subspace clustering; subspace ... See more keywords
Non-intrusive Sparse Subspace Learning for Parametrized Problems
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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
Sparse subspace linear discriminant analysis
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DOI: 10.1080/02331888.2018.1469020
Abstract: ABSTRACT We study high dimensional multigroup classification from a sparse subspace estimation perspective, unifying the linear discriminant analysis (LDA) with other recent developments in high dimensional multivariate analysis using similar tools, such as penalization method.… read more here.
Keywords: analysis; sparse subspace; linear discriminant; discriminant analysis ... 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
Fuzzy Sparse Subspace Clustering for Infrared Image Segmentation
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DOI: 10.1109/tip.2023.3263102
Abstract: Infrared image segmentation is a challenging task, due to interference of complex background and appearance inhomogeneity of foreground objects. A critical defect of fuzzy clustering for infrared image segmentation is that the method treats image… read more here.
Keywords: infrared image; subspace clustering; sparse subspace; subspace ... See more keywords