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Variance explained by the factors #34
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Unlike PCA, there is no good way to quantify the variance explained on a factor-by-factor basis, because the factors aren't orthogonal to each other. |
Excellent. Thank you so much. |
Hello, another quick question. How can I use the factors I obtained above to project a new tensor onto the space spanned by the factors? Basically, I want to find the weight vector for a new set of inputs. Is there a function implemented for this purpose? |
My advice would be to refit the model, unlike PCA there isn't a simple
projection operation that gives the optimal low-D factors. The reasons are
similar to the answer above -- the factors you extract aren't orthogonal so
instead of projecting you need to solve the alternating least-squares
problem. The review paper on tensor decompositions by Kolda & Bader 2009
may help provide some insight.
…On Tue, Nov 23, 2021 at 11:45 AM theesan16 ***@***.***> wrote:
Hello, another quick question. How can I use the factors I obtained above
to project a new tensor onto the space spanned by the factors? Basically, I
want to find the weight vector for a new set of inputs. Is there a function
implemented for this purpose?
Thank you in advance.
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I see, thanks. |
Could someone please clarify how I can calculate the total variance explained by the factors after decomposing?
I am currently using the following code:
`
U = tt.ncp_hals(my_data, rank=6, verbose=False)
factors = U.factors.factors
`
my data is a 3-dimensional tensor.
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