Online Stochastic DCA With Applications to Principal Component Analysis

Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular,...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 5; S. 7035 - 7047
Hauptverfasser:	Le Thi, Hoai An, Luu, Hoang Phuc Hau, Dinh, Tao Pham
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Big Data Computer Science Convex functions DC~algorithm (DCA) Difference of Convex functions (DC) programming Divers Learning algorithms Machine learning Machine learning algorithms Martingales Mathematics nonconvex optimization online stochastic DCA (osDCA) Optimization Principal component analysis principal component analysis (PCA) Principal components analysis Programming Stochastic processes Stochasticity Difference of Convex functions (DC) programming Online stochastic DCA (osDCA) DC algorithm (DCA) Nonconvex optimization Principal component analysis (PCA)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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