Employing deep learning and sparse representation for data classification
Seyed Mehdi Hazrati Fard, Sattar Hashemi · 2017
Selecting a proper set of features with the best discrimination is always a challenge in classification. In this paper we propose a method, named GLLC (General Locally Linear Combination), to extract features using a deep autoencoder and reconstruct a sample based on other samples in a low dimensional space, then the class with minimum reconstruction error is selected as the winner. Extracting features along with the discrimination characteristic of the sparse model can create a robust classifier that shows simultaneous reduction of samples and features. Although the main application of GLLC is in the visual classification and face recognition, it can be used in other applications. We conduct extensive experiments to demonstrate that the proposed algorithm gain high accuracy on various datasets and outperforms the state-of-the-art methods.