Metric Learning-Guided Least Squares Classifier Learning

doi:10.1109/TNNLS.2018.2830802

Metric Learning-Guided Least Squares Classifier Learning

2018-12-01

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DOI：10.1109/TNNLS.2018.2830802

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For a multicategory classification problem, discriminative least squares regression (DLSR) explicitly introduces an epsilon-dragging technique to enlarge the margin between the categories, yielding superior classification performance from a margin perspective. In this brief, we reconsider this classification problem from a metric learning perspective and propose a framework of metric learning-guided least squares classifier (MLG-LSC) learning. The core idea is to learn a unified metric matrix for the error of LSR, such that such a metric matrix can yield small distances for the same category, while large ones for the different categories. As opposed to the epsilon-dragging in DLSR, we call this the error-dragging (e-dragging). Different from DLSR and its related variants, our MLG-LSC implicitly carries out the e-dragging and can naturally reflect the roughly relative distance relationships among the categories from a metric learning perspective. Furthermore, our optimization objective functions are strictly (geodesically) convex and thus can obtain their corresponding closed-form solutions, resulting in higher computational performance. Experimental results on a set of benchmark data sets indicate the validity of our learning framework.

Keywords:

Error-dragging (e-dragging)

least squares regression (LSR)

metric learning guided

multicategory classification

Metric Learning-Guided Least Squares Classifier Learning

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