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How does on test regression for a subspace or matrix factorization?



2019 Community Moderator ElectionHow does the test data gets collected?matrix factorization?How exactly does matrix factorization help with collaborative filteringKernelized Probabilistic Matrix Factorization - Implementation?Does a matrix factorization recommendation engine use user/item related features?Regularization term in Matrix FactorizationMatrix Factorization for Recommender SystemsHow dot product limits expressiveness and leads to sub-optimal solutions in Matrix Factorization?Finding unobserved ratings using matrix factorizationDoes cardinality of ratings column affect performance of matrix factorization based collaborative filtering?










0












$begingroup$


I've recently been reading a lot of papers and watching a lot of videos on both subspace learning, and matrix factorization. One thing is particularly eluding me though - how does any of this get tested?



Let's take, straight from Wikipedia, Matrix Factorization Non-Negative.



$$V = WH$$



So, you have a data matrix $V$. Your goal is to learn components $W$ and $H$, which when multiplied together, give a good approximation of $V$. This can be done by minimizing over $W$, $H$



$$| V - WH |$$



That seems fine so far. My problem, theoretically, is understanding when we want to apply this to a problem, like say Regression.



If you wanted to minimize:



$$Y - WH*B$$



How do you do this with a test point? I get confused here, because if we had, say a 100-user test set with 10 features. Then we do a 90/10 split, we get a size of $W*H$ that is different than the size of our test data.



Do people just plug the test data in directly when testing, in place of $W*H$, and just rely on those learned weights $B$?










share|improve this question











$endgroup$
















    0












    $begingroup$


    I've recently been reading a lot of papers and watching a lot of videos on both subspace learning, and matrix factorization. One thing is particularly eluding me though - how does any of this get tested?



    Let's take, straight from Wikipedia, Matrix Factorization Non-Negative.



    $$V = WH$$



    So, you have a data matrix $V$. Your goal is to learn components $W$ and $H$, which when multiplied together, give a good approximation of $V$. This can be done by minimizing over $W$, $H$



    $$| V - WH |$$



    That seems fine so far. My problem, theoretically, is understanding when we want to apply this to a problem, like say Regression.



    If you wanted to minimize:



    $$Y - WH*B$$



    How do you do this with a test point? I get confused here, because if we had, say a 100-user test set with 10 features. Then we do a 90/10 split, we get a size of $W*H$ that is different than the size of our test data.



    Do people just plug the test data in directly when testing, in place of $W*H$, and just rely on those learned weights $B$?










    share|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      I've recently been reading a lot of papers and watching a lot of videos on both subspace learning, and matrix factorization. One thing is particularly eluding me though - how does any of this get tested?



      Let's take, straight from Wikipedia, Matrix Factorization Non-Negative.



      $$V = WH$$



      So, you have a data matrix $V$. Your goal is to learn components $W$ and $H$, which when multiplied together, give a good approximation of $V$. This can be done by minimizing over $W$, $H$



      $$| V - WH |$$



      That seems fine so far. My problem, theoretically, is understanding when we want to apply this to a problem, like say Regression.



      If you wanted to minimize:



      $$Y - WH*B$$



      How do you do this with a test point? I get confused here, because if we had, say a 100-user test set with 10 features. Then we do a 90/10 split, we get a size of $W*H$ that is different than the size of our test data.



      Do people just plug the test data in directly when testing, in place of $W*H$, and just rely on those learned weights $B$?










      share|improve this question











      $endgroup$




      I've recently been reading a lot of papers and watching a lot of videos on both subspace learning, and matrix factorization. One thing is particularly eluding me though - how does any of this get tested?



      Let's take, straight from Wikipedia, Matrix Factorization Non-Negative.



      $$V = WH$$



      So, you have a data matrix $V$. Your goal is to learn components $W$ and $H$, which when multiplied together, give a good approximation of $V$. This can be done by minimizing over $W$, $H$



      $$| V - WH |$$



      That seems fine so far. My problem, theoretically, is understanding when we want to apply this to a problem, like say Regression.



      If you wanted to minimize:



      $$Y - WH*B$$



      How do you do this with a test point? I get confused here, because if we had, say a 100-user test set with 10 features. Then we do a 90/10 split, we get a size of $W*H$ that is different than the size of our test data.



      Do people just plug the test data in directly when testing, in place of $W*H$, and just rely on those learned weights $B$?







      regression linear-regression matrix-factorisation matrix






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 5 hours ago









      Stephen Rauch

      1,52551330




      1,52551330










      asked 6 hours ago









      JibrilJibril

      1111




      1111




















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