Technical Program

Paper Detail

Paper:MLSP-P7.1
Session:Pattern Recognition and Classification II
Time:Friday, May 21, 15:30 - 17:30
Presentation: Poster
Topic: Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification
Title: DELTA-MSE DISSIMILARITY IN GLA BASED VECTOR QUANTIZATION
Authors: Mantao Xu; University of Joensuu 
Abstract: The generalized Lloyd algorithm is one of popular partition-based algorithms to construct the codebook in vector quantization. We propose the Delta-MSE dissimilarity measurement between training vectors and code vectors based on the MSE distortion function. The Delta-MSE function is heuristically derived by calculating the difference of MSE distortion before and after moving a training vector from one cluster to another. We show that the Delta-MSE dissimilarity applies also to minimizing the F-ratio validity index of the vector quantizer. We incorporate the underlying dissimilarity into the generalized Lloyd algorithm in vector quantization with the initial codebook derived from the PCA-based k-d tree algorithm. Experimental results show that the proposed dissimilarity generally achieves better performance than the L2 distance in constructing the codebook of vector quantization.
 
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