Rank–sparsity balanced representation for subspace clustering
Sign Up to like & getrecommendations! Published in 2018 at "Machine Vision and Applications"
DOI: 10.1007/s00138-018-0918-y
Abstract: Subspace learning has many applications such as motion segmentation and image recognition. The existing algorithms based on self-expressiveness of samples for subspace learning may suffer from the unsuitable balance between the rank and sparsity of… read more here.
Keywords: rank sparsity; representation; subspace; subspace clustering ... See more keywords
ASCRClu: an adaptive subspace combination and reduction algorithm for clustering of high-dimensional data
Sign Up to like & getrecommendations! Published in 2020 at "Pattern Analysis and Applications"
DOI: 10.1007/s10044-020-00884-7
Abstract: The curse of dimensionality in high-dimensional data is one of the major challenges in data clustering. Recently, a considerable amount of literature has been published on subspace clustering to address this challenge. The main objective… read more here.
Keywords: combination; high dimensional; subspace clustering; dimensional data ... See more keywords
Entropy-based active sparse subspace clustering
Sign Up to like & getrecommendations! Published in 2018 at "Multimedia Tools and Applications"
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
Sign Up to like & getrecommendations! Published in 2020 at "Neural Processing Letters"
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
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Computer Vision"
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
Multi-Featured and Fuzzy Based Dual Analysis Approach to Optimize the Subspace Clustering for Images
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DOI: 10.1007/s11277-020-07482-0
Abstract: In unsupervised classification, the subspace clustering is gaining the scope for the categorization of the more comprehensive and random image pool. In this paper, the visual and appearance features of images are evaluated independently and… read more here.
Keywords: image pool; subspace clustering; dataset; subspace ... See more keywords
Robust sequential subspace clustering via ℓ1-norm temporal graph
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DOI: 10.1016/j.neucom.2019.12.019
Abstract: Abstract Subspace clustering (SC) has been widely applied to segment data drawn from multiple subspaces. However, for sequential data, a main challenge in subspace clustering is to exploit temporal information. In this paper, we propose… read more here.
Keywords: temporal graph; subspace clustering; norm temporal; robust sequential ... See more keywords
Hub-based subspace clustering
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DOI: 10.1016/j.neucom.2020.06.098
Abstract: Abstract Data often exists in subspaces embedded within a high-dimensional space. Subspace clustering seeks to group data according to the dimensions relevant to each subspace. This requires the estimation of subspaces as well as the… read more here.
Keywords: hub based; high dimensional; subspace clustering; based subspace ... See more keywords
Support structure representation learning for sequential data clustering
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DOI: 10.1016/j.patcog.2021.108326
Abstract: Abstract Sequential data clustering is a challenging task in data mining (e.g., motion recognition and video segmentation). For good performance in dealing with complex local correlation and high-dimensional structure of sequential data, representation based methods… read more here.
Keywords: sequential data; structure representation; subspace clustering; data clustering ... See more keywords
A splitting method for the locality regularized semi-supervised subspace clustering
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DOI: 10.1080/02331934.2019.1671841
Abstract: Graph-based semi-supervised learning (G-SSL) methods play an increasingly important role in machine learning systems. Recently, latent low-rank representation (LatLRR) graph has gained great success in subspace clustering. However, LatLRR only considers the global structure, while… read more here.
Keywords: supervised subspace; locality regularized; subspace clustering; semi supervised ... See more keywords
Multi-Scale Deep Subspace Clustering With Discriminative Learning
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DOI: 10.1109/access.2022.3200482
Abstract: Deep subspace clustering methods have achieved impressive clustering performance compared with other clustering algorithms. However, most existing methods suffer from the following problems: 1) they only consider the global features but neglect the local features… read more here.
Keywords: multi scale; expressiveness coefficient; self expressiveness; subspace clustering ... See more keywords