Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
| 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. |
| |
| Back | |