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Linear Dimensionality Reduction for Multi-label Classification
| Citations: | 15 - 1 self |
BibTeX
@MISC{Ji_lineardimensionality,
author = {Shuiwang Ji and Jieping Ye},
title = {Linear Dimensionality Reduction for Multi-label Classification},
year = {}
}
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Abstract
Dimensionality reduction is an essential step in high-dimensional data analysis. Many dimensionality reduction algorithms have been applied successfully to multi-class and multi-label problems. They are commonly applied as a separate data preprocessing step before classification algorithms. In this paper, we study a joint learning framework in which we perform dimensionality reduction and multi-label classification simultaneously. We show that when the least squares loss is used in classification, this joint learning decouples into two separate components, i.e., dimensionality reduction followed by multi-label classification. This analysis partially justifies the current practice of a separate application of dimensionality reduction for classification problems. We extend our analysis using other loss functions, including the hinge loss and the squared hinge loss. We further extend the formulation to the more general case where the input data for different class labels may differ, overcoming the limitation of traditional dimensionality reduction algorithms. Experiments on benchmark data sets have been conducted to evaluate the proposed joint formulations. 1
Keyphrases
multi-label classification dimensionality reduction linear dimensionality reduction current practice loss function multi-label problem classification problem input data joint learning decouples different class label essential step general case traditional dimensionality reduction algorithm square loss joint formulation joint learning framework separate data many dimensionality reduction algorithm hinge loss separate application separate component squared hinge loss classification algorithm high-dimensional data analysis benchmark data set
