function [features,weights] = SD(features,labels,Q)
% function [features,weights] = SD(features,labels,Q)
% Estimates the statistical dependency between features and associated class labels using a quantized feature space.
%
% Inputs:
% features: N x F sized matrix of features, where N is the number of samples and F is the number of features
% labels: N x 1 sized vector of class labels corresponding to each sample
% Q: the number of quantization levels used for the features (default = 12)
%
% Outputs:
% features: F x 1 sized vector of feature indices in the
% descending order of relevance.
% weights: F x 1 sized vector of feature relevances (SDs) in the
% descending order.
%
% Author: Okko Rasanen, 2013. Mail: okko.rasanen@aalto.fi
%
% The algorithm can be freely used for research purposes.
%
% Please see J. Pohjalainen, O. Rasanen & S. Kadioglu: "Feature Selection Methods and
% Their Combinations in High-Dimensional Classification of Speaker Likability,
% Intelligibility and Personality Traits", Computer Speech and Language, 2015, for more details.
if nargin <3
Q = 12;
end
edges = zeros(size(features,2),Q+1);
% Compute feature-specific quantization bins so that each bin has approximately equal number of
% samples in the training set
for k = 1:size(features,2)
minval = min(features(:,k));
maxval = max(features(:,k));
if minval==maxval
continue;
end
quantlevels = minval:(maxval-minval)/500:maxval;
N = histc(features(:,k),quantlevels);
totsamples = size(features,1);
N_cum = cumsum(N);
edges(k,1) = -Inf;
stepsize = totsamples/Q;
for j = 1:Q-1
a = find(N_cum > j.*stepsize,1);
edges(k,j+1) = quantlevels(a);
end
edges(k,j+2) = Inf;
end
% Quantize data according to the obtained bins
S = zeros(size(features));
for k = 1:size(S,2)
S(:,k) = quantize(features(:,k),edges(k,:))+1;
end
% Compute statistical dependency (SD) between the quantized features and
% the class labels
I = zeros(size(features,2),1);
for k = 1:size(features,2)
I(k) = computeSD(S(:,k),labels,0);
end
% Sort features into descending order
[weights,features] = sort(I,'descend');
%% EOF
function [I,M,SP] = computeSD(seq1,seq2,lag)
% function [I,M,SP] = computeSD(seq1,seq2,lag)
% Computes the statistical dependency (SD) between seq1 and seq2 at the
% given delay (lag) between the sequences.
%
% Inputs:
%
% seq1: a discrete sequence of length N
% seq2: a discrete sequence of length N
% lag: the number of elements that seq1 is delayed with respect to
% seq2 (a positive or negative integer). Default = 0;
if nargin <3
lag = 0;
end
if(length(seq1) ~= length(seq2))
error('Input sequences are of different length');
end
% Count the frequency and probability of each symbol in seq1
lambda1 = max(seq1);
symbol_count1 = zeros(lambda1,1);
for k = 1:lambda1
symbol_count1(k) = sum(seq1 == k);
end
symbol_prob1 = symbol_count1./sum(symbol_count1)+0.000001;
% Count the frequency and probability of each symbol in seq2
lambda2 = max(seq2);
symbol_count2 = zeros(lambda2,1);
for k = 1:lambda2
symbol_count2(k) = sum(seq2 == k);
end
symbol_prob2 = symbol_count2./sum(symbol_count2)+0.000001;
% Compute the joint occurrence frequencies of symbol pairs at the given lag
M = zeros(lambda1,lambda2);
if(lag > 0)
for k = 1:length(seq1)-lag
loc1 = seq1(k);
loc2 = seq2(k+lag);
M(loc1,loc2) = M(loc1,loc2)+1;
end
else
for k = abs(lag)+1:length(seq1)
loc1 = seq1(k);
loc2 = seq2(k+lag);
M(loc1,loc2) = M(loc1,loc2)+1;
end
end
% Product of individual state probabilities as a matrix
SP = symbol_prob1*symbol_prob2';
% Pair joint probability
M = M./sum(M(:))+0.000001;
% Compute statistical dependency
I = sum(sum(M.*(M./SP)))-1;
function y = quantize(x, q)
x = x(:);
nx = length(x);
nq = length(q);
y = sum(repmat(x,1,nq)>repmat(q,nx,1),2);