https://github.com/aalto-ics-kepaco/mkc_software

https://github.com/aalto-ics-kepaco/mkc_software

Repository

https://github.com/aalto-ics-kepaco/MKC_software/blob/master/

# MKC
Multi-view Kernel Completion

(c) Sahely Bhadra
sahely@iitpkd.ac.in
Jun. 1, 2016.

This version is debugged by Xiangju Qin, a postdoctoral researcher in Prof. Tero Aittokallio's group at FIMM, university of Helsinki. I convey my regards to her.
April, 2019.

Details of the software are available in http://arxiv.org/abs/1602.02518

This package contains  following three version of proposed multi-view kernel completion method along with supporting function and scripts

1. MKCsdp
2. MKCapp
3. MKCemdb(ht)


Details descriare :




function [PredK,S,objective,iOutput]=MKCsdp(K,MID,para,init)

This solve MKCsdp formulation 
input :

K: kernels matrix size N x N x M. K(n1,n2,m) =kenrel values among n1 and n2 datapoints in m view

MID: a matrix of NxM {0,1} matrix where MID(n,m) = 1 indicates nth data is known in mth view

para: contain usedefined parameters : para.c1 Withinviewloss + para.c2 betweenviewloss + para.c3 L21norm regularisation

init : if init =1 then S is initialized by assignning all off-diagonal element with same values
       otherwise it S is randomly initalized  

Output:

PredK =\hat(K) : predicted kernel matrices of size NxNxM

S : Inter-view similarity matrix

objective : objective values

iOutput : intermediate objective function 

function [PredK,A,S,obj,iOutput] = MKCapp(K,MID,para,init)

This solve MKCapp formulation 

input :

K: kernels matrix size N x N x M. K(n1,n2,m) =kenrel values among n1 and n2 datapoints in m view

MID: a matrix of NxM {0,1} matrix where MID(n,m) = 1 indicates nth data is known in mth view

para: contain usedefined parameters : para.c1 Withinviewloss + para.c2 betweenviewloss + para.c3 L21norm regularisation

init : if init =1 then A^{(m)} is initialized by non diagonal selfrepresentative matrix of K^{(m)}
       otherwise it A^{(m)} is randomly initalized  

Output:

PredK =\hat(K) : predicted kernel matrices of size NxNxM

A : learnt reconstruction matrix

S : Inter-view similarity matrix

objective : objective values

iOutput : intermediate objective function 

function [PredK,A,S,obj,iOutput] = MKCemdbht(K,MID,para,init)

This solve MKCembdht formulation 
 
input :

K: kernels matrix size N x N x M. K(n1,n2,m) =kenrel values among n1 and n2 datapoints in m view

MID: a matrix of NxM {0,1} matrix where MID(n,m) = 1 indicates nth data is known in mth view

para: contain usedefined parameters : para.c1 Withinviewloss + para.c2 betweenviewloss + para.c3 L21norm regularisation

init : if init =1 then A^{(m)} is initialized by non diagonal selfrepresentative matrix of K^{(m)}
       otherwise it A^{(m)} is randomly initalized  

Output:

PredK =\hat(K) : predicted kernel matrices of size NxNxM

A : learnt reconstruction matrix

S : Inter-view similarity matrix

objective : objective values

iOutput : intermediate objective function 

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