A novel feature learning framework for high-dimensional data classification

Feature extraction is an essential component in many classification tasks. Popular feature extraction approaches especially deep learning-based methods, need large training samples to achieve satisfactory performance. Although dictionary learning-based methods are successfully used for feature extraction on both small and large datasets, however, when dealing with high-dimensional datasets, a large number of dimensions also mask the discriminative information embedded in the data. To address these issues, a novel feature learning framework for high-dimensional data classification is proposed in this paper. Specially, to discard the irrelevant parts that derail the dictionary learning process, the dictionary is adaptively learnt in the low-dimensional space parameterized by a transformation matrix. To ensure that the learned features are discriminative for the classifier, the classification results in turn are used to guide the dictionary and transformation matrix learning process. Compared with other methods, the proposed method simultaneously exploits the dimension reduction, dictionary learning and classifier learning in one optimization framework, which enables the method to extract low-dimensional and discriminative features. Experimental results on several benchmark datasets demonstrate the superior performance of the proposed method for high-dimensional data classification task, particularly when the number of training samples is small.