More matrix factorization today. PCA is bad, HMF is good. Why is PCA bad? Because it does a good job at describing the variance of your data, which can have substantial contributions from—or be dominated by—noise. Why is HMF good? Because it models the noise-deconvolved underlying distribution.
To sell this, you might want to say how "HMF" differs from factor analysis (e.g., as reviewed by Zoubin and Sam).
ReplyDeleteGood point. The only real difference is the objective function, which is annoying when it is changed from MSE to chi-squared.
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