2021/03/06 by Eysan Mehrbani, Mohammad Hossein Kahaei, Seyed Aliasghar Beheshti +2
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Cluster analysis #Combinatorics #Computer science #Data point #Face and Expression Recognition #Linear subspace #Locality #Machine Learning and ELM #Mathematics #Outlier #Pattern recognition (psychology) #Rank (graph theory) #Regularization (linguistics) #Representation (politics) #Sparse and Compressive Sensing Techniques #Subspace topology #Tensor (intrinsic definition) #Tensor decomposition and applications #cs.LG #stat.ML
paper · pdf · doi:10.1109/lsp.2021.3129686
arxiv created 2021/03/06 · openalex publication_date 2021/11/22 · arxiv updated 2022/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Low-Rank Representation (LRR) highly suffers from discarding the locality information of data points in subspace clustering, as it may not incorporate the data structure nonlinearity and the non-uniform distribution of observations over the ambient space. Thus, the information of the observational density is lost by the state-of-art LRR models, as they take a constant number of adjacent neighbors into account. This, as a result, degrades the subspace clustering accuracy in such situations. To cope with deficiency, in this paper, we propose to consider a hypergraph model to facilitate having a variable number of adjacent nodes and incorporating the locality information of the data. The sparsity of the number of subspaces is also taken into account. To do so, an optimization problem is defined based on a set of regularization terms and is solved by developing a tensor Laplacian-based algorithm. Extensive experiments on artificial and real datasets demonstrate the higher accuracy and precision of the proposed method in subspace clustering compared to the state-of-the-art methods. The outperformance of this method is more revealed in presence of inherent structure of the data such as nonlinearity, geometrical overlapping, and outliers.