Relevance Vector Machine (RVM)

versión 2.1.2 (178 KB) por Kepeng Qiu
MATLAB code for Relevance Vector Machine using SB2_Release_200.

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Actualizada 21 Feb 2022

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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.

Citation

@article{tipping2001sparse,
  title={Sparse Bayesian learning and the relevance vector machine},
  author={Tipping, Michael E},
  journal={Journal of machine learning research},
  volume={1},
  number={Jun},
  pages={211--244},
  year={2001}
}
@article{qiu2021soft,
  title={Soft sensor development based on kernel dynamic time warping and a relevant vector machine for unequal-length batch processes},
  author={Qiu, Kepeng and Wang, Jianlin and Wang, Rutong and Guo, Yongqi and Zhao, Liqiang},
  journal={Expert Systems with Applications},
  volume={182},
  pages={115223},
  year={2021},
  publisher={Elsevier}
}

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
%}

Citar como

@article{tipping2001sparse, title={Sparse Bayesian learning and the relevance vector machine}, author={Tipping, Michael E}, journal={Journal of machine learning research}, volume={1}, number={Jun}, pages={211--244}, year={2001} }

@article{qiu2021soft, title={Soft sensor development based on kernel dynamic time warping and a relevant vector machine for unequal-length batch processes}, author={Qiu, Kepeng and Wang, Jianlin and Wang, Rutong and Guo, Yongqi and Zhao, Liqiang}, journal={Expert Systems with Applications}, volume={182}, pages={115223}, year={2021}, publisher={Elsevier} }

Compatibilidad con la versión de MATLAB
Se creó con R2021a
Compatible con la versión R2016b y siguientes
Compatibilidad con las plataformas
Windows macOS Linux

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