Low-rank sparse subspace for spectral clustering

• المؤلفون الرئيسيون: ZHU, Xiaofeng, ZHANG, Shichao, LI, Yonggang, ZHANG, Jilian, YANG, Lifeng, FANG, Yue
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2019
• الموضوعات: Affinity matrix; Clustering methods; Correlation; Feature extraction; Feature selection; Laplace equations; Redundancy; Sparse matrices; spectral clustering; subspace learning; Technological innovation; Computer Engineering; Databases and Information Systems
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/sis_research/4093; https://ink.library.smu.edu.sg/context/sis_research/article/5096/viewcontent/08417928.pdf

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address...