-
Notifications
You must be signed in to change notification settings - Fork 2
/
k_ufomkl_logistic_train.m
181 lines (151 loc) · 5.76 KB
/
k_ufomkl_logistic_train.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
function model = k_ufomkl_logistic_train(K, Y, model, options)
% K_UFOMKL_LOGISTIC_TRAIN Ultra Fast Optimization for Multi Kernel Learning
%
% MODEL = K_UFOMKL_LOGISTIC_TRAIN(K,Y,MODEL) trains a sparse Multi
% Kernel binary classifier using Ultra Fast Optimization algorithm,
% using precomputed kernels. The loss function is the logistic loss.
%
% Inputs:
% K - 3-D N*N*F Kernel Matrices, each kernel K(:, :, i) is a N*N matrix
% Y - Training label, N*1 Vector
%
% Additional parameters:
% - model.alpha is the weight of the group norm (2,1) term. It regulates
% the sparsity of the solution.
% Default value is 0.01.
% - model.T is numer of training epochs for the batch stage.
% Default value is 5.
% - model.lambda is the regularization weight.
% Default value is 1/numel(Y).
%
% References:
% - Orabona, F., Jie, L. (2011).
% Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning.
% Proceedings of the 28th International Conference on Machine Learning.
% This file is part of the DOGMA library for MATLAB.
% Copyright (C) 2009-2011, Francesco Orabona
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
%
% Contact the authors: francesco [at] orabona.com
% jluo [at] idiap.ch
timerstart = cputime;
n = length(Y); % number of training samples
n_kernel = size(K,3); % number of kernels
if isfield(model,'lambda')==0
model.lambda = 1/numel(Y);
end
if isfield(model,'step')==0
model.step = 100*numel(Y);
end
if isfield(model,'alpha')==0
model.alpha = .01;
end
if isfield(model,'iter')==0
model.iter = 0;
model.aer = [];
model.epoch = 0;
model.sum_tau = 0;
end
if isfield(model, 'test')==1
model.inititer = model.test(end);
else
model.inititer = 0;
model.test = [];
model.sparse = [];
end
beta = spalloc(1, n, n);
predmat = zeros(n, n_kernel);
weights = zeros(1, n_kernel);
sqnorms = zeros(1, n_kernel)+eps;
model.time = []; % training time on each step
model.q = 2*log(n_kernel);
model.p = 1/(1-1/model.q);
if isfield(model,'T')==0
model.T = 5;
end
for epoch = 1:model.T
model.epoch = model.epoch+1;
idx_rand = randperm(n);
model.errTot = 0;
model.lossTot = 0;
n_update = 0;
for i = 1:n
model.iter = model.iter+1;
idxs_subgrad = idx_rand(i);
preds = predmat(idxs_subgrad,:);
val_f = preds(:, :)*weights';
yhat = sign(val_f);
yi = Y(idxs_subgrad);
% the exponential could overflow to infinity
if -yi*val_f>=87
loss = -yi*val_f;
eta = -yi;
else
val_exp = exp(-yi*val_f);
loss = log(1+val_exp);
eta = -yi*val_exp/(1+val_exp);
end
model.errTot = model.errTot + (yhat~=yi);
model.lossTot = model.lossTot + loss;
%elastic net
lr=model.lambda*model.iter;
if loss>0
Kii = double(K(idxs_subgrad,idxs_subgrad, :));
beta(idxs_subgrad) = beta(idxs_subgrad)-eta;
sqnorms = sqnorms - 2*eta*preds + Kii(:)';
n_update = n_update+1;
% update predmat
predmat = predmat - squeeze(eta*K(idxs_subgrad,:,:));
norms = sqrt(sqnorms);
trunc_norms = max(norms-model.iter*model.alpha,0);
norm_trunc_theta = norm(trunc_norms+eps,model.q);
weights = (trunc_norms./(norms+eps)).*((trunc_norms/norm_trunc_theta).^(model.q-2))/lr;
else
trunc_norms = max(norms-model.iter*model.alpha,0);
norm_trunc_theta = norm(trunc_norms+eps,model.q);
weights = (trunc_norms./(norms+eps)).*((trunc_norms/norm_trunc_theta).^(model.q-2))/lr;
end
if mod(model.iter+model.inititer,model.step)==0
model.test(end+1) = model.iter+model.inititer;
model.time(end+1) = cputime-timerstart;
if exist('options') && isfield(options,'eachRound')~=0
model.beta = beta;
model.sqnorms = sqnorms;
model.weights = weights;
model = feval(options.eachRound, K, Y, model, options);
end
model.sparse(end+1) = numel(find(weights==0));
timerstart = cputime;
end
end
fprintf('#%.0f(epoch %.0f)\tAER:%5.2f\tAEL:%5.2f\tUpdates:%.0f\n', ...
ceil(model.iter/1000), epoch, model.errTot/n*100, model.lossTot/n, n_update);
if epoch==model.T
model.test(end+1) = model.iter+model.inititer;
model.time(end+1) = cputime-timerstart;
if exist('options') && isfield(options,'eachRound')~=0
model.beta = beta;
model.sqnorms = sqnorms;
model.weights = weights;
model = feval(options.eachRound, K, Y, model, options);
end
model.sparse(end+1) = numel(find(weights==0));
timerstart = cputime;
end
end
model.beta = beta;
model.sqnorms = sqnorms;
model.weights = weights;
model.S = find(beta);