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WORD格式 可编辑 专业知识整理分享 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