| 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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