Material of “ CUR Algorithm for Partially Observed Matrices ”
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چکیده
∥M − PY (M)∥2 ≤ ∥Σ2∥2 + ∥Σ2Ψ2Ψ † 1∥2 where PY (M) projects column vectors in M in the subspace spanned by the column vectors in Y and † denotes the pseudoinverse. Theorem 2. (Derived From Theorem 2.2 of (Tropp, 2011)) Let X be a finite set of PSD matrices with dimension k (means the size of the square matrix is k× k). λmax(·) and λmin(·) calculate the maximum and minimum eigen value respectively.
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CUR Algorithm for Partially Observed Matrices
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تاریخ انتشار 2015