Fuzzy Subspace Hidden Markov Models for Pattern Recognition

This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.