Abstract
Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.
Original language | English |
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Article number | 8424176 |
Pages (from-to) | 5947-5956 |
Number of pages | 10 |
Journal | IEEE Transactions on Image Processing |
Volume | 27 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2018 |
Externally published | Yes |
Keywords
- Dimensionality reduction
- classification
- divergence measures
- nearest neighbor graph
- pattern recognition