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Oct 2, 2009 Low-Rank Approximation. Randomized Sparsification. Approximating Subspace Approach. Ailon and Chazelle introduced the FJLT earlier work showed you need k = O(logn), so to decrease computation, considered using sparse projection matrices problem is that sparse projection matrices distort sparse.Roller Bearings. Solid Needle Roller. Bearings. Inner Rings for Needle. Roller Bearings. Thrust Bearings. Cam Followers. Roller Followers. Needle Rollers. Spherical Plain. Bearings. Needle Roller Bearings for Universal Joints. Drawn Cup Roller. Clutches. Oil Seals for Needle. Roller Bearings. Example Figures and.

(p = 1, 2) called the fast Johnson–Lindenstrauss transform (FJLT). The FJLT is faster than standard random projections and just as easy to implement. It is based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Sparse random projections are unsuitable for low-distortion.

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