## Contents

- Description demo_unsupervised_ISOMAP.m
- usage of KPCA with rbf basis
- usage ISOMAP to visualize animals dataset in low dimensions

## Description demo_unsupervised_ISOMAP.m

Demonstrates use of ISOMAP to visualize a dataset in lower dimensions

    clear all
    close all
    load animals.mat
    [n,d] = size(X);

## usage of KPCA with rbf basis

Reduce to 2-dimensions with KPCA

    kernelArgs = struct('sigma',10);
    options = struct('maxComponents',2,'kernelFunc',@ml_kernel_rbf,...
                     'kernelArgs',kernelArgs);
    model = ml_unsupervised_dimRedKPCA(X,options);
    Xreduced = model.reduceFunc(model,X);
    plot(Xreduced(:,1),Xreduced(:,2),'.');
    grid on
    grid minor
    title('KPCA Projection onto 2-dimensions of animals data (rbf kernel)');
    gname(animals)

    Number of Components selected: 2
    Variance explained by basis: 0.22

## usage ISOMAP to visualize animals dataset in low dimensions

    options = [];
    options.K = 2;
    options.names = animals;
    options.disconnected = 1;
    ml_visualize_ISOMAP(X,options);