Entropy controlled Laplacian regularization for least square regression

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Journal Article
Signal Processing, 2010, 90 (6), pp. 2043 - 2049
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Least square regression (LSR) is popular in pattern classification. Compared against other matrix factorization based methods, it is simple yet efficient. However, LSR ignores unlabeled samples in the training stage, so the regression error could be large when the labeled samples are insufficient. To solve this problem, the Laplacian regularization can be used to penalize LSR. Extensive theoretical and experimental results have confirmed the validity of Laplacian regularized least square (LapRLS). However, multiple hyper-parameters have been introduced to estimate the intrinsic manifold induced by the regularization, and thus the time consuming cross-validation should be applied to tune these parameters. To alleviate this problem, we assume the intrinsic manifold is a linear combination of a given set of known manifolds. By further assuming the priors of the given manifolds are equivalent, we introduce the entropy maximization penalty to automatically learn the linear combination coefficients. The entropy maximization trades the smoothness off the complexity. Therefore, the proposed model enjoys the following advantages: (1) it is able to incorporate both labeled and unlabeled data into training process, (2) it is able to learn the manifold hyper-parameters automatically, and (3) it approximates the true probability distribution with respect to prescribed test data. To test the classification performance of our proposed model, we apply the model on three well-known human face datasets, i.e. FERET, ORL, and YALE. Experimental results on these three face datasets suggest the effectiveness and the efficiency of the new model compared against the traditional LSR and the Laplacian regularized least squares. © 2010 Elsevier B.V. All rights reserved.
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