Article ID Journal Published Year Pages File Type
410333 Neurocomputing 2013 9 Pages PDF
Abstract

•It proposes a local scaling heuristic-based regularization (LSHR) for binary classification.•This LSHR can reflect the intra-class compactness and inter-class separability of outputs.•The H-LSHR and LS-LSHR classifiers with the hinge and least squares loss functions are presented based on LSHR.

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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
Authors
, ,