Open AccessNon‐negative sparse subspace clustering by orthogonal matching pursuit
Author(s) -
Zhan Jiaqiyu,
Bai Zhiqiang,
Zhu Yuesheng
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3066
Subject(s) - cluster analysis , matching pursuit , subspace topology , pattern recognition (psychology) , property (philosophy) , mathematics , algorithm , computer science , matrix (chemical analysis) , graph , matching (statistics) , artificial intelligence , negativity effect , combinatorics , statistics , compressed sensing , psychology , social psychology , philosophy , materials science , epistemology , composite material
In sparse subspace clustreing (SSC), non‐zero coefficients in the sparse representations are transformed to positive values to construct the affinity matrix. However, negative coefficients are not supposed to cause positive correlation in nature, so that the clustering performance may be undermined. In this Letter, non‐negative SSC by orthogonal matching pursuit (nSSCOMP) with a negative re‐purpose module is introduced for a reasonable clustering graph to improve the clustering performance, also an assemble negatives of negatives algorithm is proposed to strengthen the effectiveness of affinity matrix. The analysis indicates that the authors' approach can make the coefficient matrix contains only non‐negative values and bring additional positive correlations. Their approach is the first to process negative values after sparse representations are already calculated, without specify non‐negativity constraints beforehand. Experimental results demonstrate with the nSSCOMP not only the clustering accuracy and subspace‐preserving property can be improved, but also time complexity be kept.