Balinsky, Alexander ![]() |
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
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.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Mathematics |
Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Uncontrolled Keywords: | Semi-supervised learning; compressive sampling; heavy-tailed distributions; sparsity |
Publisher: | IEEE |
ISBN: | 9781424427932 |
Last Modified: | 19 Oct 2022 10:28 |
URI: | https://orca.cardiff.ac.uk/id/eprint/24448 |
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