Marinka Zitnik
- Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 7291 (2010), 993--998.Google Scholar
Cross Ref
- Lee, D. D., and Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755(1999), 788--791.Google ScholarCross Ref
- Lee, D. D., and Seung, H. S. Algorithms for non-negative matrix factorization. NIPS (2001), 556--562.Google Scholar
- Žitnik, M., and Zupan, B. Nimfa: A Python library for nonnegative matrix factorization. The Journal of Machine Learning Research 13, 1 (2012), 849--853. Google ScholarDigital Library
- Kim, H., and Park, H. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23, 12(2007), 1495--1502. Google ScholarDigital Library
Index Terms
- Hidden Genes: Understanding cancer data with matrix factorization
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