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In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results. [ABSTRACT FROM AUTHOR]

Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed. Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue. In this paper, we propose a new ensemble learning algorithms for multilabel data. The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification. Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained. Finally, a binary classifier for each label is constructed on the top features. Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms. [ABSTRACT FROM AUTHOR]

One of the main challenges faced by biological applications is to predict protein subcellular localization in an automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological system into account. Traditionally such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressive Sensing), we propose a method which performs locality preserving projection with a sparseness criterion such that the feature selection and dimension reduction are merged into one analysis. The proposed sparse method decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins. [ABSTRACT FROM AUTHOR]