Question

knn based classification with pcasvd reduction technique apply it for classification.function parts are nedded.

Write a matlab code

if you know the answer then only write.or else leave it for others.

image processing ELETRICAL

Answer 1

ans:

function result = knnclassification(testsamplesX,samplesX, samplesY, Knn,type)

% Classify using the Nearest neighbor algorithm

% Inputs:

% samplesX - Train samples

% samplesY - Train labels

% testsamplesX - Test samples

% Knn - Number of nearest neighbors

%

% Outputs

% result - Predicted targets

if nargin < 5

type = '2norm';

end

L = length(samplesY);

Uc = unique(samplesY);

if (L < Knn),

error('You specified more neighbors than there are points.')

end

N = size(testsamplesX, 1);

result = zeros(N,1);

switch type

case '2norm'

for i = 1:N,

dist = sum((samplesX - ones(L,1)*testsamplesX(i,:)).^2,2); %rowwise sum

[m, indices] = sort(dist);  

n = hist(samplesY(indices(1:Knn)), Uc);

[m, best] = max(n);

result(i) = Uc(best);

end

case '1norm'

for i = 1:N,

dist = sum(abs(samplesX - ones(L,1)*testsamplesX(i,:)),2);

[m, indices] = sort(dist);

n = hist(samplesY(indices(1:Knn)), Uc);

[m, best] = max(n);

result(i) = Uc(best);

end

case 'match'

for i = 1:N,

dist = sum(samplesX == ones(L,1)*testsamplesX(i,:),2);

[m, indices] = sort(dist);

n = hist(samplesY(indices(1:Knn)), Uc);

[m, best] = max(n);

result(i) = Uc(best);

end

otherwise

error('Unknown measure function');

end

%% RUN command:- result = knnclassification(featureVectorNorm,featureVectorNorm, targetVector, 1,'2norm')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% this is sometimes faster than the transpose eigenvalue method with

% very large dimensions and small no. of examples

function [U,d] = pcasvd(X,n,smallest)

% X : nt x n, where nt examples of feature vecs of size n

% n returns n dimensions

% smallest is 1 when we want the evecs assoc with the smallest evals

% % U is evecs, d is diagonals

% Copyright (c) 2013, Vipin Vijayan.

if nargin < 3, smallest = 0; end;

[U,S] = svd(X,'econ');

s = diag(S);

if ~smallest,

tol = max(size(X)) * max(s) * eps(class(X));

n = min(n, sum(s > tol));

U = U(:,1:n);

s = s(1:n);

else

n = min(n, size(U,2));

U = U(:,end:-1:end-n+1);

s = s(end:-1:end-n+1);

end

d = s.^2; % D = diag(s.^2);

end