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Tensor Toolbox for dense, sparse, and decomposed n-way arrays.
cp_als - Compute a CP decomposition of any type of tensor.
ALS交替最小二乘法求张量CP分解
P = CP_ALS(X,R)——计算张量X秩为R的最佳近似CP分解,P=[P.lambda, P.U]
P = CP_ALS(X,R,param,value,...)选择参数设置
tol - Tolerance on difference in fit {1.0e-4}
maxiters - Maximum number of iterations {50}
dimorder - Order to loop through dimensions {1:ndims(A)}
init - Initial guess [{random}|nvecs|cell array]
printitn - Print fit every n iterations; 0 for no printing {1}
[P,U0,out] = CP_ALS(...) also returns additional output that contains the input parameters.
Note: The fit is defined as 1 - norm(X-full(P))/norm(X) and is loosely the proportion of the data described by the CP model, i.e., a fit of 1 is perfect.
% Examples:
% X = sptenrand([5 4 3], 10);
% P = cp_als(X,2);
% P = cp_als(X,2,dimorder,[3 2 1]);
% P = cp_als(X,2,dimorder,[3 2 1],init,nvecs);
% U0 = {rand(5,2),rand(4,2),[]}; %-- Initial guess for factors of P
% [P,U0,out] = cp_als(X,2,dimorder,[3 2 1],init,U0);
% P = cp_als(X,2,out.params); %-- Same params as previous run
交替泊松回归求张量X的非负CP分解
cp_apr - Compute nonnegative CP with alternating Poisson regression.
M = CP_APR(X, R) computes an estimate of the best rank-R
M = CP_APR(X, R, param, value, ...) specifies optional parameters and values. Valid parameters and their default values are:
tol - Tolerance on the inner KKT violation {1.0e-4}
maxiters - Maximum number of iterations {1000}
maxinneriters = Maximum number of inner iterations {10}
init - Initial guess [{random}|ktensor]
epsilon - parameter to avoid divide by zero {100*eps}
kappatol - tolerance on complementary slackness {100*eps}
kappa - offset to fix complementary slackness {10*eps}
printitn - Print every n outer iterations; 0 for no printing {1}
printinneritn - Print every n inner iteratio
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