Fast Non-Negative Dimensionality Reduction for Protein Fold Recognition
Abstract
In this paper, dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix factorization in combination with a classifier for protein fold recognition. Since typically matrix factorization is iteratively done, convergence can be slow. To alleviate this problem, a significantly faster (more than 11 times) algorithm is proposed.
Cite
Text
Okun et al. "Fast Non-Negative Dimensionality Reduction for Protein Fold Recognition." European Conference on Machine Learning, 2005. doi:10.1007/11564096_67Markdown
[Okun et al. "Fast Non-Negative Dimensionality Reduction for Protein Fold Recognition." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/okun2005ecml-fast/) doi:10.1007/11564096_67BibTeX
@inproceedings{okun2005ecml-fast,
title = {{Fast Non-Negative Dimensionality Reduction for Protein Fold Recognition}},
author = {Okun, Oleg and Priisalu, Helen and Alves, Alexessander},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {665-672},
doi = {10.1007/11564096_67},
url = {https://mlanthology.org/ecmlpkdd/2005/okun2005ecml-fast/}
}