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Contents1 Singular Value Decomposition (SVD) Singular Vectors .. Singular Value Decomposition (SVD) .. Best RankkApproximations .. Power Method for Computing the Singular Value Decomposition .. Applications of Singular Value Decomposition .. Component Analysis .. a Mixture of Spherical Gaussians .. Application of SVD to a Discrete Optimization Problem .. as a Compression Algorithm .. Decomposition .. Vectors and ranking documents .. Bibliographic Notes .. Exercises.
unit vector along this line. The length of the projection of a i;the ith row of A, onto v is ja i vj:From this we see that the sum of length squared of the projections is jAvj2.The best t line is the one maximizing jAvj2 and hence minimizing the sum of the squared distances of the points to the line.
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