On use of different feature sets for pattern classification: an alternative method

We propose an alternative method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g. combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets, a modular neural network architecture is proposed to implement the idea accordingly. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation. Moreover, we propose a heuristic model selection method to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.