Streaming Sparse Principal Component Analysis
نویسندگان
چکیده
1. Preliminaries Theorem A-1. (Theorem 3.1, (Chang, 2012)) Let A ∈ Rm×n be of full column rank with QR factorization A = QR, ∆A be a perturbation in A, and A + ∆A = (Q + ∆Q)(R + ∆R) be the QR-factorization of A + ∆A. Let PA and PA⊥ be the orthogonal projectors onto the range of A and the orthogonal complement of the range of A, respectively. LetQ⊥ be an orthonormal matrix such that matrix [Q,Q⊥] is orthogonal. Define κ2(A) = ∥A∥2∥A∥2, whereA† is the Moore-Penrose pseudo-inverse ofA. If
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