基于自适应加权的多视图聚类算法matlab版本

function [Y, p, obj] = AWP(Fs, opts) % solve: % min_{R_i, Y} \sum_i || Y - F_i R_i ||_F % s.t. R_i^T R_i = I, Y \in Ind % % paras: % Fs 1xv cell, contain nxk spectral embedding (F_i) % opts options for debug and parameter setting % Y nx1 indicator vector % p vx1 vector % % Ref: Multiview Clustering via Adaptively Weighted Procrustes % Written by: Lai Tian, tianlai.cs@gmail.com % 2018-01-20 if (~exist('opts', 'var')) opts = []; end NITER = 20; if (isfield(opts, 'NITER')) NITER = opts.NITER; end v = length(Fs); [n, k] = size(Fs{1}); % == initialization == %Y = sparse(1:n, randi(k, n, 1), ones(n, 1), n, k); Y = zeros(n, k); Rs = cell(v, 1); for idx = 1:v; Rs{idx} = eye(k); end p = ones(v, 1); for iter = 1:NITER % == update Y == sum_pFR = zeros(size(Y)); for idx = 1:v sum_pFR = sum_pFR + Fs{idx}*Rs{idx}/p(idx); end [~, y_idx] = max(sum_pFR, [], 2); Y = full(sparse(1:n, y_idx, ones(n, 1), n, k)); % == update R_i == for idx = 1:v [tmp_u, ~, tmp_v] = svd(Y'*Fs{idx}); Rs{idx} = tmp_v * tmp_u'; end % == update p_i == for idx = 1:v p(idx) = norm(Y - Fs{idx}*Rs{idx}, 'fro'); end obj(iter) = norm(p); end % == from indicator mat to vec == [~, Y] = max(Y, [], 2); end