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Multilinear Subspace Clustering [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2015 In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union ...
Aeron, Shuchin +3 more
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Projection subspace clustering [PDF]
Journal of Algorithms & Computational Technology, 2017 Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension.
Xiaoyun Chen, Mengzhen Liao, Xianbao Ye
doaj +2 more sources
CUR Decompositions, Similarity Matrices, and Subspace Clustering [PDF]
Frontiers in Applied Mathematics and Statistics, 2019 A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U=⋃Mi=1Si.
Akram Aldroubi +3 more
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LogDet Rank Minimization with Application to Subspace Clustering. [PDF]
Comput Intell Neurosci, 2015 Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator.
Kang Z, Peng C, Cheng J, Cheng Q.
europepmc +5 more sources
Robust auto-weighted multi-view subspace clustering with common subspace representation matrix. [PDF]
PLoS ONE, 2017 In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly.
Wenzhang Zhuge +5 more
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Learnable Subspace Clustering [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 This paper studies the large-scale subspace clustering (LSSC) problem with million data points. Many popular subspace clustering methods cannot directly handle the LSSC problem although they have been considered as state-of-the-art methods for small-scale data points. A basic reason is that these methods often choose all data points as a big dictionary
Jun Li +4 more
openaire +3 more sources
Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering [PDF]
Jisuanji gongcheng, 2021 The potential subspace structure of high-dimensional data can be obtained by using subspace clustering,but the existing methods can not reveal the characteristics of global low-rank structure and local sparse structure of data at the same time,which ...
TAO Yang, BAO Linglang, HU Hao
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Multi-Layer Network Community Detection Based on Sparse Subspace Clustering [PDF]
Jisuanji gongcheng, 2021 The existing subspace clustering methods are only applicable to single-layer networks, or just average the clustering results of each layer in the multi-layer network.They fail to consider the different amounts of information contained in each layer ...
SUN Dengdi, LING Yuan, DING Zhuanlian, LUO Bin
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Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
Applied Sciences, 2022 Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects.
Yiqiang Duan +3 more
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PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark
IEEE Access, 2021 Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc.
Xiao Wen, Hu Juan
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