Abstract
In non-negative matrix factorization, it is difficult to find the optimal non-negative factor matrix in each iterative update. However, with the help of transformation matrix, it is able to derive the optimal non-negative factor matrix for the transformed cost function. Transformation matrix based nonnegative matrix factorization method is proposed and analyzed. It shows that this new method, with comparable complexity as the priori schemes, is efficient in enhancing nonnegative matrix factorization and achieves better performance in NMF based text clustering.
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Li, F., Zhu, Q. (2010). A Novel Fast Non-negative Matrix Factorization Algorithm and Its Application in Text Clustering . In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_49
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DOI: https://doi.org/10.1007/978-3-642-13498-2_49
Publisher Name: Springer, Berlin, Heidelberg
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