PCA-KL: a parametric dimensionality reduction approach for unsupervised metric learning
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DOI: 10.1007/s11634-020-00434-3
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- Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
- Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
- Lan Huong Nguyen & Susan Holmes, 2019. "Ten quick tips for effective dimensionality reduction," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-19, June.
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Keywords
Dimensionality reduction; PCA; KL-divergence; Unsupervised Metric learning;All these keywords.
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