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A A X X X X X X

The document presents a matrix X with m rows and n columns that represents data from m samples across n features. It then defines a probability distribution PA over the samples with each sample having a probability of 1/m. This defines a weighted average of the feature values for each sample. Another matrix Tp contains the true feature values for each sample and matrix Tr contains the reconstructed feature values. Their difference G represents the error matrix.

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Abhishek Kumar
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437 views5 pages

A A X X X X X X

The document presents a matrix X with m rows and n columns that represents data from m samples across n features. It then defines a probability distribution PA over the samples with each sample having a probability of 1/m. This defines a weighted average of the feature values for each sample. Another matrix Tp contains the true feature values for each sample and matrix Tr contains the reconstructed feature values. Their difference G represents the error matrix.

Uploaded by

Abhishek Kumar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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C1 C2 ...

Cn

A1  x11 x12 ... x1n 


A2 x x22 ... x2 n 
X =  21
...  ... ... ... ... 
 
An  xm1 xm 2 ... xmn 
;
m
1
𝑃𝐴𝑖 = ; ∑ PAi = 1 , 𝑖 = 1,2 … . 𝑚
𝑚
i=1
P

w1 w2 ... wn

PA1  PA1 .w1 PA1 .w2 ... PA1 .wn 


PA2  P .w P .w ... PA2 .wn 
 A2 1 A2 2
...
 ... ... ... ... 
PAm  
 PAm .w1 PAm .w2 ... PAm .wn 
PA1  t p1 1 t p1 2 ... t p1n 
PA2 t t p2 2 ... t p2 n 
 p2 1
...  ... ... ... ... 
 
PAm t pm1 t pm 2 ... t pmn 

 g11 g12 ... g1n 


g g 22 ... g 2 n 
G  T p  Tr  
21

 ... ... ... ... 


 
 g m1 g m2 ... g mn 
 t p  tr11 t p12  t r12 ... t p  t r1n 
 11 
1n

 t p 21  tr21 t p  t r22 ... t p  tr2 n 



... 
22 2n

... ... ...


 
t p m1  t rm 2 tp
m2
 trm 3 ... t p  trmn 
mn 

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