Online mixture-based clustering for high dimensional count data using Neerchal–Morel distribution

Abstract

In this paper, we propose an online learning technique for unsupervised clustering based on a mixture of Neerchal–Morel distributions (NMD). Online learning is able to overcome the drawbacks of batch learning in such a way that the mixture’s parameters can be updated instantly for any new data instances. Then, we use a novel Minorization–Maximization framework to address the issue of high dimensional optimization and the mixture’s parameters estimation. Finally, by implementing a minimum message length model selection criterion, the weights of irrelevant mixture components are driven towards zero, which resolves the problem of knowing the number of clusters beforehand. To evaluate the performance of our proposed model, we have considered 3 challenging real-world applications that involve high-dimensional count vectors, namely, topic clustering, medical diagnosis and human action recognition. The results show that the mixture model based on the NMD performs better than other similar models.