Sequential Data Clustering

An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Poin...

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Bibliographic Details
Main Authors: Jianfei Wu, Nimer, L A, Azzam, O A, Chitraranjan, C, Salem, S, Denton, Anne M
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Equations
Gaussian Mixture model
Kernel
KL-divergence
Mathematical model
Point Distribution algorithm
Random projection
Speech
Symmetric matrices
Time series analysis
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Description
Summary:An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed for mining patterns between vector and item data. The subsequent clustering procedure is based on fitting a Gaussian mixture model on multiple random projection matrices. The final class label of each unit is determined by voting from the results of the random projection matrices.
DOI:10.1109/ICMLA.2010.161