eprintid: 399130
rev_number: 107
eprint_status: archive
userid: 608
dir: disk0/00/39/91/30
datestamp: 2010-12-13 22:05:01
lastmod: 2021-09-28 22:26:22
status_changed: 2015-11-25 16:03:48
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Sun, S
creators_name: Shawe-Taylor, J
title: Sparse Semi-supervised Learning Using Conjugate Functions
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: semi-supervised learning, Fenchel-Legendre conjugate, representer theorem, multiview regularization, support vector machine, statistical learning theory
note: Copyright © 2010 Shiliang Sun and John Shawe-Taylor.
abstract: In this paper, we propose a general framework for sparse semi-supervised learning, which concerns
using a small portion of unlabeled data and a few labeled data to represent target functions and thus
has the merit of accelerating function evaluations when predicting the output of a new example.
This framework makes use of Fenchel-Legendre conjugates to rewrite a convex insensitive loss
involving a regularization with unlabeled data, and is applicable to a family of semi-supervised
learning methods such as multi-view co-regularized least squares and single-view Laplacian support
vector machines (SVMs). As an instantiation of this framework, we propose sparse multi-view
SVMs which use a squared ε-insensitive loss. The resultant optimization is an inf-sup problem and
the optimal solutions have arguably saddle-point properties. We present a globally optimal iterative
algorithm to optimize the problem. We give the margin bound on the generalization error of the
sparse multi-view SVMs, and derive the empirical Rademacher complexity for the induced function
class. Experiments on artificial and real-world data show their effectiveness. We further give a
sequential training approach to show their possibility and potential for uses in large-scale problems
and provide encouraging experimental results indicating the efficacy of the margin bound and empirical
Rademacher complexity on characterizing the roles of unlabeled data for semi-supervised
learning
date: 2010-09
official_url: http://www.jmlr.org/papers/v11/sun10a.html
vfaculties: VENG
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_source: WoS-Lite
elements_id: 280280
lyricists_name: Shawe-Taylor, John
lyricists_id: JSHAW87
full_text_status: public
publication: Journal of Machine Learning Research
volume: 11
pagerange: 2423 - 2455
issn: 1532-4435
citation:        Sun, S;    Shawe-Taylor, J;      (2010)    Sparse Semi-supervised Learning Using Conjugate Functions.                   Journal of Machine Learning Research , 11    2423 - 2455.          Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/399130/1/Shawe-Taylor_sun10a%255B1%255D.pdf