data science certification in Bangalore


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Uploaded on Sep 7, 2018

Category Education

Data Science certification training course from ExcelR equips you with essential Data Science skills to make you a successful Data Scientist. Register now and grab your seats before they left.

Category Education

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data science certification in Bangalore

Microsoft PowerPoint - 2-SVD Singular Value Decomposition (SVD) Matrix Transpose Matrix Multiplication Matrix Inverse                 = =× 1 0 ... 0 0 0 ...1 0 0 . ... . . . . . . 0 0 ... 1 0 0 0 ... 0 1 :matrixIdentity A 1-B Then, matrixidentity , If, IBA X= UΣVT rxr diagonal matrix with r non-zero diagonal elements U and V are Orthonormal matrices OPTIONAL R Code > M=matrix(c(1,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,2,0,0,0),nrow=4,ncol=5) > X=svd(M) > X$u > X$d > X$v > X$u%*%diag(X$d)%*%t(X$v) 1 0 0 0 2 0 0 1 0 4 0 0 0 0 1 0 0 0 0 0 3 0 0 0 1 0 0 0 3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 -1 0 0 2.24 0 0.45 0 0 0 0.89 0 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 V T = x x X U ∑ Applications of SVD in image processing – closest rank-k approximation for a matrix X – Each term in the summation expression above is called principal image vuX Tii k i i k Σ∑ = = 1 Original matrix (X) Original size 1 0 0 0 2 4*5=20 bytes 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 1 0 0 0 2 0 0 1 0 4 0 0 0 0 1 0 0 0 0 0 3 0 0 0 1 0 0 0 3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 -1 0 0 2.24 0 0.45 0 0 0 0.89 0 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 V T = x x X U ∑ k=1 0 x 4 x 0 1 0 0 0 = 0 0 0 0 0 Compressed size 0 0 0 0 0 0 4*1+1+1*5=10 bytes 0 0 0 0 0 0 1 0 4 0 0 0 k=2 0 0 x 4 0 x 0 1 0 0 0 = 0 0 0 0 0 Compressed size 0 1 0 3 0 0 1 0 0 0 0 3 0 0 4*2+2+2*5=20 bytes 0 0 0 0 0 0 0 1 0 0 4 0 0 0 k=3 0 0 1 x 4 0 0 x 0 1 0 0 0 = 1 0 0 0 2 Compressed size 0 1 0 0 3 0 0 0 1 0 0 0 0 3 0 0 4*3+3+3*5=30 bytes 0 0 0 0 0 2.24 0.45 0 0 0 0.89 0 0 0 0 0 1 0 0 0 4 0 0 0 k=4 0 0 1 0 4 0 0 0 0 1 0 0 0 = 1 0 0 0 2 Compressed size 0 1 0 0 0 3 0 0 0 0 1 0 0 0 0 3 0 0 4*4+4+4*5=40 bytes 0 0 0 -1 0 0 2.24 0 0.45 0 0 0 0.89 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 4 0 0 0 x x The image compression example in http://journal.batard.info/post/2009/04/08/svd- fun-profit • Original size = 384*384 bytes = 147,456 bytes • k=1: 384*1+1+1*384=769 bytes • k=10: 384*10+10+10*384=7,690 bytes • k=20: 384*20+20+20*384=15,380 bytes • k=50: 384*50+50+50*384=38,450 bytes • k=100: 384*100+100+100*384=76,900 bytes • k=200: 384*200+200+200*384=153,800 bytes