Research Article
Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications to Blind Source Separation
Algorithm 1
Learning algorithm.
Algorithm parameters: ; | Input: an n by m nonnegative observation matrix V; | Output: an n by r nonnegative matrix W and an r by m | nonnegative
matrix H. | Step 1: set , and generate nonnegative matrix and | at random; | Step 2: Update from to by | | | where I is an r by m matrix full of elements being 1s, and M | is an r by r matrix with all elements being 1s except diagonal | elements being
zeros. | Step 3: Increment t by and go to step 2 until | and converge. |
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