An Improved Subspace Clustering Algorithm Based on Sparse Representation

Wang, Xiaohe; Wang, Yuping; Wang, Chunyang; Tang, Dandan

doi:10.1109/cis.2019.00054

Cited by 1 publication

(1 citation statement)

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“…Structured sparse subspace clustering (S 3 C) lets affinity matrix learning stage and subspace clustering stage depend on each other and improves the performance of affinity learning by available partial sideinformation [14]. Sparse representation-based improved subspace clustering considers both sparseness and grouping effect [15]. SSC model with TV regularization (LSSC-TV) accelerates the clustering speed of traditional SSC algorithm and uses the total variation property of images for obtaining strong robustness to noise [16].…”

Section: Introductionmentioning

confidence: 99%

Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm

Liao

Liu

Xie

2022

J. Phys.: Conf. Ser.

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Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.

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Section: Introductionmentioning

confidence: 99%