Low-Rank Structure Learning via Nonconvex Heuristic Recovery

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity i...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 24; H. 3; S. 383 - 396
Hauptverfasser:	Deng, Yue, Dai, Qionghai, Liu, Risheng, Zhang, Zengke, Hu, Sanqing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.03.2013 Institute of Electrical and Electronics Engineers
Schlagworte:	Adaptation models Applied sciences Artificial intelligence Clustering algorithms Compressive sensing Computer science; control theory; systems Data processing. List processing. Character string processing Detection, estimation, filtering, equalization, prediction Exact sciences and technology Heuristic algorithms Information, signal and communications theory Learning and adaptive systems log-sum heuristic matrix learning Memory organisation. Data processing Minimization nuclear norm minimization Optimization Signal and communications theory Signal processing algorithms Signal, noise Software Sparse matrices sparse optimization Telecommunications and information theory Structure learning log-sum heuristic Partial ordering Minimization nuclear norm minimization Modeling Optimization Subsampling Stationary condition matrix learning Upper bound Experimental result Compressive sensing sparse optimization Heuristic method Sparse representation Objective function Data structure Principal component analysis Non convex analysis
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!