A Robust Hidden Markov Model Based Clustering Algorithm
Hidden Markov models (HMMs) are widely employed in sequential data modeling both because they are capable of handling multivariate data of varying length, and because they capture the underlying hidden properties of time-series. Over the years, HMM-based clustering methods have been widely investigated and improved. However, their performance on noisy data and the effectiveness of similarity measure between sequences remain less explored. In this paper, we present a robust algorithm for sequential data clustering by combining spectral analysis with HMMs. We first derive Fisher kernels from continuous density HMMs for similarity matrix construction, and then apply spectral clustering algorithm to the mapped data. The eigenvector decomposition step in spectral analysis is critical for noise removal and dimensionality reduction. Experimental results on both synthetic and real-world data indicate that our proposed approach is more tolerant to noise and achieves improved accuracy compared to many state-of-the-art algo rithms.
HMM Spectral Clustering Fisher Kernel
Shitong Yao
Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240, China
国际会议
重庆
英文
762-767
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）