Name
Abstract | ||
|---|---|---|
This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set. In this way, the vector quantization output can keep as much information of the class label as possible. An objective function is constructed and we also developed an iterative algorithm to minimize it. The new method is evaluated on bag-of-features based image classification problem. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Divergence,Linde–Buzo–Gray algorithm,Pattern recognition,Iterative method,Computer science,Learning vector quantization,Vector quantization,Artificial intelligence,Quantization (image processing),Quantization (signal processing),Kullback–Leibler divergence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1501.07681 | 0 |
PageRank | References | Authors |
0.34 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lan Yang | 1 | 3 | 5.54 |
| Jingbin Wang | 2 | 1 | 2.03 |
| Yujin Tu | 3 | 1 | 0.68 |
| Prarthana Mahapatra | 4 | 0 | 0.34 |
| Nelson Cardoso | 5 | 0 | 0.34 |