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I came to know that various ML algorithms can be posed a matrix factorization problems with different constraints specific to that particular problem. Is there any good material that provides an overview for frequently used ML algorithms(PCA,Kmeans etc ) at one place.

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Generalized Low Rank Models paper deals with exactly this.

From the abstract:

This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization.

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