Local scaling heuristic-based regularization for pattern classification

doi:10.1016/j.neucom.2013.03.032

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Local scaling heuristic-based regularization for pattern classification

2013-11-01

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Xinjun Peng *

Dong Xu

DOI：10.1016/j.neucom.2013.03.032

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Abstract

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In this paper, a novel regularization method called the local scaling heuristic-based regularization (LSHR) is proposed for binary classification. The idea in LSHR is to integrate the underlying knowledge inside the training points, including the intra-class and inter-class local information in training points. By combining the local scaling heuristic strategy, this LSHR uses two matrices defined on the intra-class and inter-class graphs of points to reflect the intra-class compactness and inter-class separability of outputs. Based on the LSHR method, two classifiers with the hinge and least squares loss functions, H-LSHR and LS-LSHR, are presented for binary classification. The experimental results on several artificial, UCI benchmark datasets and USPS digit datasets indicate the effectiveness of the proposed method. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Keywords:

Pattern recognition

Regularization

Intra-class compactness

Inter-class separability

Local scaling heuristic