| Title | : | Non-Negative Matrix Factorization (NMF) | Multiplicative Update Rules By Lee And Seung |
| Lasting | : | 2.45 |
| Date of publication | : | |
| Views | : | 27 rb |
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what does "not always positive semi-definite" mean and how it is related to solvability of the equation? Is there a nice video/website that anyone can recommend for it? Comment from : @yuzdeotuzz |
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mersi ahmad joon ;)) Comment from : @forooghfarajzade8206 |
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What is the advantage of non-negative matrix factorization over matrix factorization without the constraint? Comment from : @ThePritt12 |
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Very cool video Just a doubt on how do we choose K ? Comment from : @abhishekpatil9599 |
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👍 Comment from : @gara2322 |
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2:20 I know this learning rates lead to multiplicative update rules and guarantee non-negativity, but would it be possible that the learning rates are too large at some iteration to guarantee the convergence of NMF? Comment from : @asdfqwer2988 |
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@Ahmad Varasteh I think there might be an error @2:19 for the "W" equation on the bottom right: the numerator should be "XH^T" rather than "W^T X"brYour github code has the correct version Comment from : @seungyunsong2798 |
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Good job bro Comment from : @ShaidaMuhammad |
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Thanks a lot ; Please do you have an idea ; how to optimize a boolean matrix factorization (Binary matrices)? Comment from : @khamishoufar1019 |
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Thanks👌🌹 Comment from : @user-is8ht9lp1s |
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