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Regularized partial least squares for multi-label learning
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DOI：10.1007/s13042-016-0500-8.png)
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Abstract
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In reality, data objects often belong to several different categories simultaneously, which are semantically correlated to each other. Multi-label learning can handle and extract useful information from such kind of data effectively. Since it has a great variety of potential applications, multi-label learning has attracted widespread attention from many domains. However, two major challenges still remain for multi-label learning: high dimensionality and correlations of data. In this paper, we address the problems by using the technique of partial least squares (PLS) and propose a new multi-label learning method called rPLSML (regularized Partial Least Squares for Multi-label Learning). Specifically, we exploit PLS discriminant analysis to identify a latent and common space from the variable and label spaces of data, and then construct a learning model based on the latent space. To tackle the multi-collinearity problem raised from the high dimensionality, a l(2)-norm penalty is further exerted on the optimization problem. The experimental results on public data sets show that rPLSML has better performance than the state-of-the-art multi-label learning algorithms.
Keywords:
Multi-label learning
Partial least squares discriminant analysis
L-2-norm penalty
Dimension reduction
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