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Efficient iterative Semi-Supervised Classification on manifold
, Article Proceedings - IEEE International Conference on Data Mining, ICDM ; 2011 , Pages 228-235 ; 15504786 (ISSN); 9780769544090 (ISBN) ; Rabiee, H. R ; Shaban, A ; Soltani Farani, A ; National Science Foundation (NSF) - Where Discoveries Begin; University of Technology Sydney; Google; Alberta Ingenuity Centre for Machine Learning; IBM Research ; Sharif University of Technology
Abstract
Semi-Supervised Learning (SSL) has become a topic of recent research that effectively addresses the problem of limited labeled data. Many SSL methods have been developed based on the manifold assumption, among them, the Local and Global Consistency (LGC) is a popular method. The problem with most of these algorithms, and in particular with LGC, is the fact that their naive implementations do not scale well to the size of data. Time and memory limitations are the major problems faced in large-scale problems. In this paper, we provide theoretical bounds on gradient descent, and to overcome the aforementioned problems, a new approximate Newton's method is proposed. Moreover, convergence...