SVD , Low Rank Approximation 9 - 3 9 . 2 . 2 Application 1 : Consumer - Product Matrix
نویسندگان
چکیده
ui = Mvi ‖Mvi‖ = Mvi √ λi = Mvi σi Note that singular values σi are equal to √ λi; since M M is PSD, λi ≥ 0 and σi is well defined. In particular, observe that if M is a symmetric matrix, σi is the absolute value of the i-th eigenvalue of M . Now, we want to show that these vis and uis meet SVD conditions. Recall that vi’s are orthonormal because they are eigenvectors of MM , and uis are orthonormal by (9.1).
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