image thumbnail

Relevance Vector Machine (RVM)

version 2.1.1 (178 KB) by Kepeng Qiu
MATLAB code for Relevance Vector Machine using SB2_Release_200.

758 Downloads

Updated 16 Oct 2021

From GitHub

View license on GitHub

Relevance Vector Machine (RVM)

MATLAB code for Relevance Vector Machine

Version 2.1, 31-AUG-2021

Email: iqiukp@outlook.com

View Relevance Vector Machine (RVM) on File Exchange


Main features

  • RVM model for binary classification (RVC) or regression (RVR)
  • Multiple kinds of kernel functions (linear, gaussian, polynomial, sigmoid, laplacian)
  • Hybrid kernel functions (K =w1×K1+w2×K2+...+wn×Kn)
  • Parameter Optimization using Bayesian optimization, Genetic Algorithm, and Particle Swarm Optimization

Notices

  • This version of the code is not compatible with the versions lower than R2016b.
  • Detailed applications please see the demonstrations.
  • This code is for reference only.

How to use

01. Classification using RVM (RVC)

A demo for classification using RVM

clc
clear all
close all
addpath(genpath(pwd))

% use fisheriris dataset
load fisheriris
inds = ~strcmp(species, 'setosa');
data_ = meas(inds, 3:4);
label_ = species(inds);
cvIndices = crossvalind('HoldOut', length(data_), 0.3);
trainData = data_(cvIndices, :);
trainLabel = label_(cvIndices, :);
testData = data_(~cvIndices, :);
testLabel = label_(~cvIndices, :);

% kernel function
kernel = Kernel('type', 'gaussian', 'gamma', 0.2);

% parameter
parameter = struct( 'display', 'on',...
                    'type', 'RVC',...
                    'kernelFunc', kernel);
rvm = BaseRVM(parameter);

% RVM model training, testing, and visualization
rvm.train(trainData, trainLabel);
results = rvm.test(testData, testLabel);
rvm.draw(results)

results:

*** RVM model (classification) train finished ***
running time            = 0.1604 seconds
iterations              = 20 
number of samples       = 70 
number of RVs           = 2 
ratio of RVs            = 2.8571% 
accuracy                = 94.2857%


*** RVM model (classification) test finished ***
running time            = 0.0197 seconds
number of samples       = 30 
accuracy                = 96.6667%

02. Regression using RVM (RVR)

A demo for regression using RVM

clc
clear all
close all
addpath(genpath(pwd))

% sinc funciton
load sinc_data
trainData = x;
trainLabel = y;
testData = xt;
testLabel = yt;

% kernel function
kernel = Kernel('type', 'gaussian', 'gamma', 0.1);

% parameter
parameter = struct( 'display', 'on',...
                    'type', 'RVR',...
                    'kernelFunc', kernel);
rvm = BaseRVM(parameter);

% RVM model training, testing, and visualization
rvm.train(trainData, trainLabel);
results = rvm.test(testData, testLabel);
rvm.draw(results)

results:

*** RVM model (regression) train finished ***
running time            = 0.1757 seconds
iterations              = 76 
number of samples       = 100 
number of RVs           = 6 
ratio of RVs            = 6.0000% 
RMSE                    = 0.1260
R2                      = 0.8821
MAE                     = 0.0999


*** RVM model (regression) test finished ***
running time            = 0.0026 seconds
number of samples       = 50 
RMSE                    = 0.1424
R2                      = 0.8553
MAE                     = 0.1106

03. Kernel funcions

A class named Kernel is defined to compute kernel function matrix.

%{
        type   -
        
        linear      :  k(x,y) = x'*y
        polynomial  :  k(x,y) = (γ*x'*y+c)^d
        gaussian    :  k(x,y) = exp(-γ*||x-y||^2)
        sigmoid     :  k(x,y) = tanh(γ*x'*y+c)
        laplacian   :  k(x,y) = exp(-γ*||x-y||)
    
    
        degree -  d
        offset -  c
        gamma  -  γ
%}
kernel = Kernel('type', 'gaussian', 'gamma', value);
kernel = Kernel('type', 'polynomial', 'degree', value);
kernel = Kernel('type', 'linear');
kernel = Kernel('type', 'sigmoid', 'gamma', value);
kernel = Kernel('type', 'laplacian', 'gamma', value);

For example, compute the kernel matrix between X and Y

X = rand(5, 2);
Y = rand(3, 2);
kernel = Kernel('type', 'gaussian', 'gamma', 2);
kernelMatrix = kernel.computeMatrix(X, Y);
>> kernelMatrix

kernelMatrix =

    0.5684    0.5607    0.4007
    0.4651    0.8383    0.5091
    0.8392    0.7116    0.9834
    0.4731    0.8816    0.8052
    0.5034    0.9807    0.7274

04. Hybrid kernel

A demo for regression using RVM with hybrid_kernel (K =w1×K1+w2×K2+...+wn×Kn)

clc
clear all
close all
addpath(genpath(pwd))

% sinc funciton
load sinc_data
trainData = x;
trainLabel = y;
testData = xt;
testLabel = yt;

% kernel function
kernel_1 = Kernel('type', 'gaussian', 'gamma', 0.3);
kernel_2 = Kernel('type', 'polynomial', 'degree', 2);
kernelWeight = [0.5, 0.5];
% parameter
parameter = struct( 'display', 'on',...
                    'type', 'RVR',...
                    'kernelFunc', [kernel_1, kernel_2],...
                    'kernelWeight', kernelWeight);
rvm = BaseRVM(parameter);

% RVM model training, testing, and visualization
rvm.train(trainData, trainLabel);
results = rvm.test(testData, testLabel);
rvm.draw(results)

05. Parameter Optimization for single-kernel-RVM

A demo for RVM model with Parameter Optimization

clc
clear all
close all
addpath(genpath(pwd))

% use fisheriris dataset
load fisheriris
inds = ~strcmp(species, 'setosa');
data_ = meas(inds, 3:4);
label_ = species(inds);
cvIndices = crossvalind('HoldOut', length(data_), 0.3);
trainData = data_(cvIndices, :);
trainLabel = label_(cvIndices, :);
testData = data_(~cvIndices, :);
testLabel = label_(~cvIndices, :);

% kernel function
kernel = Kernel('type', 'gaussian', 'gamma', 5);

% parameter optimization
opt.method = 'bayes'; % bayes, ga, pso
opt.display = 'on';
opt.iteration = 20;

% parameter
parameter = struct( 'display', 'on',...
                    'type', 'RVC',...
                    'kernelFunc', kernel,...
                    'optimization', opt);
rvm = BaseRVM(parameter);

% RVM model training, testing, and visualization
rvm.train(trainData, trainLabel);
results = rvm.test(trainData, trainLabel);
rvm.draw(results)

results:

*** RVM model (classification) train finished ***
running time            = 13.3356 seconds
iterations              = 88 
number of samples       = 70 
number of RVs           = 4 
ratio of RVs            = 5.7143% 
accuracy                = 97.1429%
Optimized parameter  table

    gaussian_gamma
    ______________

        7.8261    

*** RVM model (classification) test finished ***
running time            = 0.0195 seconds
number of samples       = 70 
accuracy                = 97.1429%

06. Parameter Optimization for hybrid-kernel-RVM

A demo for RVM model with Parameter Optimization

%{
    A demo for hybrid-kernel RVM model with Parameter Optimization
%}


clc
clear all
close all
addpath(genpath(pwd))

% data
load UCI_data
trainData = x;
trainLabel = y;
testData = xt;
testLabel = yt;

% kernel function
kernel_1 = Kernel('type', 'gaussian', 'gamma', 0.5);
kernel_2 = Kernel('type', 'polynomial', 'degree', 2);

% parameter optimization
opt.method = 'bayes'; % bayes, ga, pso
opt.display = 'on';
opt.iteration = 30;

% parameter
parameter = struct( 'display', 'on',...
                    'type', 'RVR',...
                    'kernelFunc', [kernel_1, kernel_2],...
                    'optimization', opt);
rvm = BaseRVM(parameter);

% RVM model training, testing, and visualization
rvm.train(trainData, trainLabel);
results = rvm.test(testData, testLabel);
rvm.draw(results)

results:

*** RVM model (regression) train finished ***
running time            = 24.4042 seconds
iterations              = 377 
number of samples       = 264 
number of RVs           = 22 
ratio of RVs            = 8.3333% 
RMSE                    = 0.4864
R2                      = 0.7719
MAE                     = 0.3736
Optimized parameter  1×6 table

    gaussian_gamma    polynomial_gamma    polynomial_offset    polynomial_degree    gaussian_weight    polynomial_weight
    ______________    ________________    _________________    _________________    _______________    _________________

        22.315             13.595               44.83                  6               0.042058             0.95794     




*** RVM model (regression) test finished ***
running time            = 0.0008 seconds
number of samples       = 112 
RMSE                    = 0.7400
R2                      = 0.6668
MAE                     = 0.4867

07. Cross Validation

In this code, two cross-validation methods are supported: 'K-Folds' and 'Holdout'. For example, the cross-validation of 5-Folds is

parameter = struct( 'display', 'on',...
                    'type', 'RVC',...
                    'kernelFunc', kernel,...
                    'KFold', 5);

For example, the cross-validation of the Holdout method with a ratio of 0.3 is

parameter = struct( 'display', 'on',...
                    'type', 'RVC',...
                    'kernelFunc', kernel,...
                    'HoldOut', 0.3);

08. Other option

%% custom optimization option
%{      
    opt.method = 'bayes'; % bayes, ga, pso
    opt.display = 'on';
    opt.iteration = 20;
    opt.point = 10;

    % gaussian kernel function
    opt.gaussian.parameterName = {'gamma'};
    opt.gaussian.parameterType = {'real'};
    opt.gaussian.lowerBound = 2^-6;
    opt.gaussian.upperBound = 2^6;

    % laplacian kernel function
    opt.laplacian.parameterName = {'gamma'};
    opt.laplacian.parameterType = {'real'};
    opt.laplacian.lowerBound = 2^-6;
    opt.laplacian.upperBound = 2^6;

    % polynomial kernel function
    opt.polynomial.parameterName = {'gamma'; 'offset'; 'degree'};
    opt.polynomial.parameterType = {'real'; 'real'; 'integer'};
    opt.polynomial.lowerBound = [2^-6; 2^-6; 1];
    opt.polynomial.upperBound = [2^6; 2^6; 7];

    % sigmoid kernel function
    opt.sigmoid.parameterName = {'gamma'; 'offset'};
    opt.sigmoid.parameterType = {'real'; 'real'};
    opt.sigmoid.lowerBound = [2^-6; 2^-6];
    opt.sigmoid.upperBound = [2^6; 2^6];
%}

%% RVM model parameter
%{
    'display'    :   'on', 'off'
    'type'       :   'RVR', 'RVC'
    'kernelFunc' :   kernel function
    'KFolds'     :   cross validation, for example, 5
    'HoldOut'    :   cross validation, for example, 0.3
    'freeBasis'  :   'on', 'off'
    'maxIter'    :   max iteration, for example, 1000
%}
MATLAB Release Compatibility
Created with R2021a
Compatible with R2016b and later releases
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.