MATLAB最大均值差异(Maximum Mean Discrepancy)
作者:凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/
更多内容,请看标签:MATLAB、聚类
注:X与Y数据维度必须一致!
1. MMD介绍

2. MATLAB程序
数据
注:数据集仅供参考,并不能真正用于研究中。
- 源域:
- 2.1789 1.7811 5.079 4.9312
- 0.8621 2.1287 4.9825 2.3388
- 2.6347 1.9563 4.5392 4.8442
- 2.7179 2.9001 4.9027 4.8582
- 2.6686 1.6799 4.3792 4.6411
- 1.6736 2.3081 4.8384 3.2979
- 1.5666 2.6467 5.0504 4.459
- -0.5611 2.2365 4.3925 5.1316
- 5.6693 1.7355 4.5335 4.6407
- 3.2032 2.103 4.1948 5.2605
- 3.3525 2.8301 4.6383 5.6972
- -1.0407 3.5198 4.7106 4.9243
- 3.9229 2.1161 4.5666 1.772
- 2.5607 3.802 4.2681 4.6322
- 3.3072 2.5083 4.6095 2.2236
- 2.7121 2.4338 4.136 2.2348
- 5.3547 2.1088 4.402 4.9884
- 1.8302 1.4921 4.6216 3.5862
- 2.8891 2.1286 4.6419 3.8606
- -0.0896 2.6894 3.6843 6.6392
- 3.1404 1.9461 4.2604 5.9859
- 2.3406 3.1988 5.0872 4.7518
- 2.5067 2.9704 4.2749 4.3441
- 8.2153 1.7592 5.2409 3.8201
- 0.3027 2.7589 3.9826 4.8484
- 4.0223 1.7566 4.6219 4.92
- 6.1367 2.1098 4.7832 5.4567
- 4.9795 2.418 4.7726 3.1959
- -1.0746 2.4311 4.7683 4.5599
- 5.4939 2.6046 4.4663 5.1159
- 4.5709 1.9838 4.9596 4.9317
- 1.3746 2.6845 5.1921 3.2068
- 1.7178 0.7976 4.6948 3.7012
- 目标域:
- 1.9584 2.0242 4.7594 2.587
- -2.8342 3.4594 4.4371 5.2375
- 1.6251 2.7737 5.0145 6.3262
- 0.7016 2.5265 4.8881 3.2105
- 3.5579 2.5773 4.856 4.283
- 4.3282 2.7581 4.7095 6.715
- 3.1619 2.5427 4.1323 5.5883
- 4.9933 2.2985 3.8455 3.8381
- 3.2214 2.6478 4.3276 2.5246
- -0.2848 2.5853 4.6481 3.4857
- 2.876 1.5096 3.9921 2.4505
- 0.8559 2.5633 5.483 3.0589
- 4.2149 2.6618 4.2017 3.3713
MMD
- function mmd_XY=my_mmd(X, Y, sigma)
- %Author:kailugaji
- %Maximum Mean Discrepancy 最大均值差异 越小说明X与Y越相似
- %X与Y数据维度必须一致, X, Y为无标签数据,源域数据,目标域数据
- %mmd_XY=my_mmd(X, Y, 4)
- %sigma is kernel size, 高斯核的sigma
- [N_X, ~]=size(X);
- [N_Y, ~]=size(Y);
- K = rbf_dot(X,X,sigma); %N_X*N_X
- L = rbf_dot(Y,Y,sigma); %N_Y*N_Y
- KL = rbf_dot(X,Y,sigma); %N_X*N_Y
- c_K=1/(N_X^2);
- c_L=1/(N_Y^2);
- c_KL=2/(N_X*N_Y);
- mmd_XY=sum(sum(c_K.*K))+sum(sum(c_L.*L))-sum(sum(c_KL.*KL));
- mmd_XY=sqrt(mmd_XY);
Guassian Kernel
- function H=rbf_dot(X,Y,deg)
- %Author:kailugaji
- %高斯核函数/径向基函数 K(x, y)=exp(-d^2/sigma), d=(x-y)^2, 假设X与Y维度一样
- %Deg is kernel size,高斯核的sigma
- [N_X,~]=size(X);
- [N_Y,~]=size(Y);
- G = sum((X.*X),2);
- H = sum((Y.*Y),2);
- Q = repmat(G,1,N_Y(1));
- R = repmat(H',N_X(1),1);
- H = Q + R - 2*X*Y';
- H=exp(-H/2/deg^2); %N_X*N_Y
结果
- >> mmd_XY=my_mmd(x, y, 4)
-
- mmd_XY =
-
- 0.1230