Details

Autor(en) / Beteiligte

• Shen, Qiangqiang
• Liang, Yongsheng
• Yi, Shuangyan
• Zhao, Jiaoyan

Titel

Fast Universal Low Rank Representation

Ist Teil von

• IEEE transactions on circuits and systems for video technology, 2022-03, Vol.32 (3), p.1262-1272

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• As well known, low rank representation method (LRR) has obtained promising performance for subspace clustering, and many LRR variants have been developed, which mainly solve the three problems existing in LRR: 1) Problem of mean calculation; 2) Problem of deviating from the real low rank solution; 3) Problem of high computation cost on the large-scale data. In this paper, we first propose a universal LRR method referring to the first two problems. More specifically, we introduce the ability of removing the optimal mean automatically into LRR and extend it to a Schatten <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-norm minimization problem. Then, referring to the third problem, we reformulate the universal LRR version as an equivalent fast optimization version by removing the null space of data. More importantly, the effective theory proof is proposed to guarantee that the fast optimization method can dramatically improve the algorithmic computation efficiency on the large-scale data but without any loss of information. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method.