PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

L1-Norm Regularization Path for Sparse Semi-Supervised Laplacian SVM
Gilles Gasso, Karina Zapien and Stéphane Canu
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) 2007.

Abstract

Using unlabeled data to unravel the structure of the data to leverage the learning process is the goal of semi supervised learning. A common way to represent this underlying structure is to use graphs. Flexibility of the maximum margin kernel framework allows to model graph smoothness and to build kernel machine for semi supervised learning such as Laplacian SVM [1]. But a common complaint of the practitioner is the long running time of these kernel algorithms for classification of new points. We provide an efficient way of alleviating this problem by using a L1 penalization term and a regularization path algorithm to efficiently compute the solution. Empirical evidence shows the benefit of the algorithm.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3875
Deposited By:Karina Zapien
Deposited On:25 February 2008