A Novel Vector Representation of Stochastic Signals Based on Adapted Ergodic HMMs
In this letter, we propose a novel vector representation of stochastic signals for pattern recognition (PR) based on adapted ergodic hidden Markov models (HMMs). This vector representation is generic in nature and may be used with various types of stochastic signals (e.g., image, speech, etc.) and applied to a broad range of PR tasks (e.g., classification, regression, etc.). More importantly, by combining the vector representation with optimal distance metric learning (e.g., linear discriminant analysis) directly from the data, the performance of a PR system may be significantly improved. Our experiments on an image-based recognition task clearly demonstrate the effectiveness of the proposed vector representation of stochastic signals for potential use in many PR systems.