Active Subspace: Toward Scalable Low-Rank Learning

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on...

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Veröffentlicht in:	Neural computation 2012-12, Vol.24 (12), p.3371-3394
Hauptverfasser:	Liu, Guangcan, Yan, Shuicheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computer applications Lagrange multiplier Learning Letters Mathematical problems Matrix Optimization algorithms Scalability Transformation
Online-Zugang:	Volltext
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