Technical Reports


On semi-supervised learning and sparsity

Balinsky, Alexander; Balinsky, Helen
HP Laboratories


Keyword(s): semi-supervised learning, compressive sampling, sparsity of filter response

Abstract: In this article we establish a connection between semi-supervised learning and compressive sampling. We show that sparsity and compressibility of the learning function can be obtained from heavy-tailed distributions of filter responses or coefficients in spectral decompositions. In many cases the NP-hard problems of finding sparsest solutions can be replaced by l1-problems from convex optimisation theory, which provide effective tools for semi-supervised learning. We present several conjectures and examples.

6 Pages

Additional Publication Information: To be published and presented at 2009 IEEE International Conference on Systems, Man, and Cybernetics, October 11-14, 2009, San Antonio, Texas, USA

External Posting Date: August 21, 2009 [Abstract Only]. Approved for External Publication - External Copyright Consideration
Internal Posting Date: August 21, 2009 [Fulltext]

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