Pattern classification formulated as a missing data task: The audio genre classification case

Abstract

This paper presents pattern classification to a predefined set of classes as a missing data task. This is achieved by first augmenting the feature vector of each training pattern with the corresponding binary codeword representing its class. A Restricted Boltzmann Machine (RBM) or a Dictionary Learning (DL) algorithm is then trained on the augmented feature space. During the classification stage, the binary codeword of the unknown pattern is treated as missing data. In the case of the RBM, it is filled in by means of an alternating Gibbs sampling procedure. In the case of the DL method, the set of atoms in the dictionary is first learned from the training data, and the label of the unknown pattern is predicted based on those atoms that represent this pattern. Application of the method in an audio genre classification task verifies that the obtained results are highly competitive compared with state-of-the-art methods. Moreover, the DL approach lends itself readily for online implementations, in line with the current trend in big data applications.