SS-ELM和US-ELM的程序代码
1. SS-ELM(半监督极限学习机)和US-ELM(无监督极限学习机)简介
SS-ELM和US-ELM是极限学习机(ELM)的两种变种,分别用于半监督和无监督学习任务。这些方法通过引入半监督和无监督学习机制,提高了模型在有限标记数据或无标记数据情况下的学习能力。
2. SS-ELM和US-ELM的MATLAB代码实现
2.1 SS-ELM代码实现
以下是一个简化的SS-ELM的MATLAB代码实现示例:
function [P_cond, stepnum] = step1(P_cond)
P_size = length(P_cond);
for ii = 1:P_size
rmin = min(P_cond(ii,:));
P_cond(ii,:) = P_cond(ii,:) - rmin;
end
stepnum = 2;
end
function [r_cov, c_cov, M, stepnum] = step2(P_cond)
P_size = length(P_cond);
r_cov = zeros(P_size, 1);
c_cov = zeros(P_size, 1);
M = zeros(P_size);
for ii = 1:P_size
for jj = 1:P_size
if P_cond(ii, jj) == 0 && r_cov(ii) == 0 && c_cov(jj) == 0
M(ii, jj) = 1;
r_cov(ii) = 1;
c_cov(jj) = 1;
end
end
end
stepnum = 3;
end
function [c_cov, stepnum] = step3(M, P_size)
c_cov = sum(M, 1);
if sum(c_cov) == P_size
stepnum = 7;
else
stepnum = 4;
end
end
function [M, r_cov, c_cov, Z_r, Z_c, stepnum] = step4(P_cond, r_cov, c_cov, M)
P_size = length(P_cond);
zflag = 1;
while zflag
row = 0; col = 0; exit_flag = 1;
ii = 1; jj = 1;
while exit_flag
if P_cond(ii, jj) == 0 && r_cov(ii) == 0 && c_cov(jj) == 0
row = ii;
col = jj;
exit_flag = 0;
end
jj = jj + 1;
if jj > P_size; jj = 1; ii = ii + 1; end
if ii > P_size; exit_flag = 0; end
end
if row == 0
stepnum = 6;
zflag = 0;
Z_r = 0;
Z_c = 0;
else
M(row, col) = 2;
if sum(find(M(row, :)==1)) ~= 0
r_cov(row) = 1;
zcol = find(M(row, :)==1);
c_cov(zcol) = 0;
else
stepnum = 5;
zflag = 0;
Z_r = row;
Z_c = col;
end
end
end
end
2.2 US-ELM代码实现
US-ELM的实现与SS-ELM类似,但更侧重于无监督学习。以下是一个简化的US-ELM的MATLAB代码实现示例:
function [P_cond, stepnum] = step6(P_cond, r_cov, c_cov)
a = find(r_cov == 0);
b = find(c_cov == 0);
minval = min(min(P_cond(a, b)));
P_cond(find(r_cov == 1), :) = P_cond(find(r_cov == 1), :) + minval;
P_cond(:, find(c_cov == 0)) = P_cond(:, find(c_cov == 0)) - minval;
stepnum = 4;
end
function cnum = min_line_cover(Edge)
[r_cov, c_cov, M, stepnum] = step2(Edge);
[c_cov, stepnum] = step3(M, length(Edge));
[M, r_cov, c_cov, Z_r, Z_c, stepnum] = step4(Edge, r_cov, c_cov, M);
cnum = length(Edge) - sum(r_cov) - sum(c_cov);
end
3. 资源获取
这些资源提供了详细的代码实现和运行结果,适合需要进行半监督和无监督学习的研究人员和开发者。