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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?
$begingroup$
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 $B$?
regression linear-regression matrix-factorisation matrix
$endgroup$
add a comment |
$begingroup$
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 $B$?
regression linear-regression matrix-factorisation matrix
$endgroup$
add a comment |
$begingroup$
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 $B$?
regression linear-regression matrix-factorisation matrix
$endgroup$
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
regression linear-regression matrix-factorisation matrix
edited 5 hours ago
Stephen Rauch
1,52551330
1,52551330
asked 6 hours ago
JibrilJibril
1111
1111
add a comment |
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