MBB-team/VBA-toolbox

Variational Bayesian Analysis (VBA)

Volterrafaces Face Recognition System

Discriminant Analysis Using Volterra Kernels

was published in the following paper, please cite it if you find this code useful:Ritwik Kumar, Arunava Banerjee and Baba C. Vemuri, Volterrafaces: Discriminant Analysis using Volterra Kernels”, IEEE

Fast Volterra Filtering

Fast algorithm for computing the response of a discrete Volterra model given a input sequence.

output of the % p-th homogeneous nonlinear system,% i.e., the multidimensional convolution% of the input with the kernel kp.% To get the overall output of the % Volterra filter, useyP = sum(y,1);Note: The

Volterra Integral Equations Solver

Analytical Solutions of Volterra Integral Equations.

Solve Volterra Integral Equations with Difference Kernel (Convolution) using Laplace Transform.

Numerical Techniques for Volterra Equations

Numerical Volterra Equation Solver

To solve the Volterra Integral Equation with DIFFERENCE KERNEL numerically using Trapezoidal Rule of Integration. You can also compare the numerical and exact solutions. Exact solution is obtained

Separate Kernel in 1D kernels

Decompose an arbitrary N dimensional filtering kernel into 1D kernels, for faster filtering

This function SEPARATEKERNEL will separate ( do decomposition of ) any 2D, 3D or nD kernel into 1D kernels. Of course only a sub-set of Kernels are separable such as a Gaussian Kernel, but it

Violin Plot

Violin Plot based on kernel density estimation, using default ksdensity

This function creates simple violin plots by estimating the kernel density, using matlabs default ksdensity(). Given a matrix or table with m columns, you will get violins for each of the columns

Kernel decomposition

This function does the decomposition of a separable nD kernel into its 1D components.

This function does the decomposition of a separable nD kernel intoits 1D components, such that a convolution with each of thesecomponents yields the same result as a convolution with the full

Lotka-Volterra Predator Prey Model

Plots a phase portrait and time series of the Lotka-Volterra model

Matlab program to plot a phase portrait of the Lotka-Volterra Predator Prey model. In addition, the user is given the option of plotting a time series graph for x or y. Equations are solved using a

kernel density estimation

fast and accurate state-of-the-art bivariate kernel density estimator

fast and accurate state-of-the-art bivariate kernel density estimator with diagonal bandwidth matrix. The kernel is assumed to be Gaussian. The two bandwidth parameters are chosen optimally without

SVM using various Kernels

Performance of various Kernels for SVM classification

Refer: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini and John Shawe-Taylor] The training algorithm only depend on the data through dot

Kernel Density Estimator

Reliable and extremely fast kernel density estimator for one-dimensional data

Reliable and extremely fast kernel density estimator for one-dimensional data; Gaussian kernel is assumed and the bandwidth is chosen automatically; Unlike many other implementations

Kernel Smoothing Regression

A non-parametrical regression (smoothing) tool using Gaussian kernel.

expectation of a random variable:E(Y|X) = f(X)where f is a non-parametric function.Based on the kernel density estimation technique, this code implements the so called Nadaraya-Watson kernel regression

Kernel PCA

Non-linear dimension reduction using kernel PCA.

This technique takes advantage of the kernel trick that can be used in PCA. This is a tutorial only and is slow for large data sets.In line 30 the kernel can be changed. Any Kernel should do it.Ref :

Kernel Ridge Regression in Matlab

Kernel Ridge Regression

This code implements Kernel Ridge Regression, just run main.m, there is also a function to generate some polynomial toy data and randomly partition the data into training and validation data. Create

Fractional Order Chaotic Systems

Numerical solutions of the fractional order chaotic systems.

's system,-Newton-Leipnik's system,-Rossler's system,-Lotka-Volterra system,-Duffing's system,-Van der Pol's oscillator,-Volta's system,-Lu's system,-Liu's system,-Chua's systems,-Financial system,-3 cells CNN.The functions

Second Order Volterra-LMS Filter

Nonlinear System Identification using Second-Order Adaptive Volterra Filters: A #Generic Code

In this code, we will identify a nonlinear system using the traditional second-order adaptive Volterra filter. These type of filters are also known as linear-in-the-parameters nonlinear adaptive

Bivariate kernel density and regression

Bivariate kernel density, kernel regression, and kernel quantile regression

Returns, for two data series:Marginal kernel densitiesBivariate kernel densityConditional kernel densityNadaraya-Watson kernel regressionkernel quantile regressionMethod: Gaussian kernel, Silverman

MP3 a medical imaging toolbox (MRI, CT, PET...)

Medical software for Processing multi-Parametric images Pipelines

Volterra-Wiener Characterization

Volterra-Wiener characterization of non-linear dynamic systems

function:function y=Volterra_Wiener_Forecast(x,h0,h1,h2)Inputx: input signal of size (N*1)h0: zero order kernelh1: first oder kernel of size(MaxLag1+1,1)h2: second order kernel of size(MaxLag2+1,MaxLag2+1)outputy

Kernel Kmeans

kernel kmeans algorithm

This function performs kernel kmeans algorithm. When the linear kernel (i.e., inner product) is used, the algorithm is equivalent to standard kmeans algorithm. Several nonlinear kernel functions are

Kernel Density Estimator for High Dimensions

fast multivariate kernel density estimation for high dimensions

Fast adaptive kernel density estimation in high dimensions in one m-file. Provides optimal accuracy/speed trade-off, controlled via a parameter "gam"; To increase speed for "big data" applications

Kernel PCA and Pre-Image Reconstruction

standard PCA, Gaussian kernel PCA, polynomial kernel PCA, pre-image reconstruction

Kernel PCA and Pre-Image Reconstruction OverviewIn this package, we implement standard PCA, kernel PCA, and pre-image reconstruction of Gaussian kernel PCA.We also provide three demos:Two concentric

Kernel PCA

Kernel PCA analysis with Kernel ridge regression & SVM regression

Refer to 6.2.1 KPCA, Kernel Methods for Pattern Analysis, John Shawe-Taylor University of Southampton, Nello Cristianini University of California at Davis Refer to 6.2.2 Kernel Ridge Regression, An

Kernel Ridge Regression

Kernel Ridge Regression using various Kernels

Refer to 6.2.2 Kernel Ridge Regression, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Nello Cristianini and John Shawe-Taylor Refer to 7.3.2 Kernel Methods for

Kernel Adaptive Filtering Toolbox

A Matlab benchmarking toolbox for kernel adaptive filtering

Kernel adaptive filters are online machine learning algorithms based on kernel methods. Typical applications include time-series prediction, nonlinear adaptive filtering, tracking and online learning

Chebpack

The MATLAB package Chebpack solves specific problems for differential or integral equations.

Lotka Volterra & Oregonator Using GUI

Use GUI to present the solution of nonlinear dynamic problems

Solves the Lotka Volterra and Oregonator problems using GUI to present the results. The results show a limit cycle typical of nonlinear dynamic problems. This work was performed as a small three

Kernel Wiener Filter (Kernel Dependency Estimation)

The kernel Wiener Filter (kernel Dependency Estimation) algorithm in MATLAB.

The kernel Wiener Filter (kernel Dependency Estimation) in MATLAB.Note: The algorithm dependes on the eigenvalue decomposition, thus only a few thousand of data samples for training dataset is

Kernel Learning Toolbox

Machine Learning with kernels

This package provides functions that manipulate data in kernel space, such as centerization in kernel space, computing distance from kernel, etc. Many kernel algorithms for machine learning are

Local Linear Kernel Regression

A function to provide local linear estimator of Gaussian kernel regression

This is the local linear version of the kernel smoothing regression function: http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=19195&objectType=FILEThe local linear

MATLAB-Kernel-PCA

MATLAB Kernel PCA: PCA with training data , projection of new data

KernelPca.m is a MATLAB class file that enables you to do the following three things with a very short code. 1.fitting a kernel pca model with training-data with the three kernel functions (gaussian

Essential MATLAB

Companion software for Essential MATLAB for Scientists and Engineers

Kernel Ridge Regression

This is ridge regression implemented using the Gaussian Kernel.

The Gaussian Kernel can be changed to any desired kernel. However such a change will not dramatically improve results. This is a variant of ridge regression using the kernel trick (Mercers Theorem).

gibbonCode/GIBBON

GIBBON: The Geometry and Image-Based Bioengineering add-ON for MATLAB

Weisfeiler-Lehman Optimal Assignment Kernel

Suit of algorithms implementing the Weisfeiler-Lehman Optimal Assignment kernel on graphs

Ensemble of algorithms implementing the Weisfeiler-Lehman Optimal Assignment kernel for graph classification tasks. The algorithms are based on the publication, N. Kriege, P.-L. Giscard, R. C. Wilson

Kernel density estimation for circular functions

Performs kernel density estimates over arbitrary periodic domains.

See also http://dylan-muir.com/articles/circular_kernel_estimation/circ_ksdensity - Compute a kernel density estimate over a periodic domainUsage: [vfEstimate] = circ_ksdensity(vfObservations

adaptive kernel density estimation in one-dimension

fast and reliable adaptive kernel density estimator

Fast adaptive kernel density estimation in one-dimension in one m-file; Provides optimal accuracy/speed trade-off. To increase speed when dealing with "big data", simply reduce the "gam" parameter

Rotating Kernel Transformation (Lee & Rhodes)

Filter emphasising the shape of the kernel (eg straight line) within an image

The Rotating Kernel Transformation convolves an image with several orientations of a kernel.For a line-kernel, straight lines are emphasized. For larger kernels, it becomes computationally

Kernel Principal Component Analysis (KPCA)

MATLAB code for dimensionality reduction, fault detection, and fault diagnosis using KPCA.

Kernel Principal Component Analysis (KPCA)MATLAB code for dimensionality reduction, fault detection, and fault diagnosis using KPCAVersion 2.2, 14-MAY-2021Email: iqiukp@outlook.comMain

gypsilab

Fast numerical method for protyping in matlab (FEM, BEM, H-Matrix, ray-tracing, etc.)

Efficient Kernel Smoothing Regression using KD-Tree

Efficiency improved multivariant kernel regression using kd-tree

Kernel regression is a power full tool for smoothing, image and signal processing, etc. However, it is computationally expensive when it is extented for multivariant cases. The efficiency can be

Magic kernel resizing

The “magic kernel” is a method of resampling images that gives clear results free of artifacts.

The “magic kernel” is a method of resampling images that gives amazingly clear results (free of “aliasing” artifacts, free of “ringing”, and free of “width beat” for thin features) yet is lightning

The kernel-based grey system model

The kernel-based grey system model published in "Applied Mathematical Modelling"

The full publication information is:Xin Ma, Zhi-bin Liu, The kernel-based nonlinear multivariate grey model, Applied Mathematical Modelling, 2018, 56, 217-238. The publication is available at the

Matrix-Regularized Multiple Kernel Learning via (r,p) Norms.

This code implements a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r, p) norms.

This code implements a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r, p) norms. This extends vector ℓ p-norm regularization and helps explore the dependences

Relevance Vector Machine (RVM)

MATLAB code for Relevance Vector Machine using SB2_Release_200.

(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

Kernel graph cut image segmentation

Kernel graph cut segmentation according to the formulation in M. Ben Salah et al., IEEE TIP, 2011.

This code implements multi-region graph cut image segmentation accordingto the kernel-mapping formulation in M. Ben Salah, A. Mitiche, andI. Ben Ayed, Multiregion Image Segmentation by Parametric

Kernel-Based Mixture of Experts Models For Linear Regression

Kernel-Based Mixture of Experts Models For Linear Regression

This code implements a novel kernel-based mixture of experts model for linear regression. The method is novel in that it formulates the mixture of experts model for linear regression so that kernel

Edge detection methods based on oriented half kernels

Edge detection methods based on oriented half kernels

Thin filters, rotated in all the desired directions are usefulto detect edges, or extract precisely their orientations. These kernels are easy to use and reliable in image analysis.Different filters

Support Vector Regression

On-line support vector regression (using Gaussian kernel)

On-line regression On-line learning algorithms are not restricted to classification problems. The update rule for the kernel adatron algorithm also suggests a general methodology for creating on-line

Stacked line plot

Stacked line plots from a matrix or vectors

ISO 226:2003 Normal equal-loudness-level contours

Return sound pressure levels of pure tone frequencies at specified loudness level(s).

Quantile-quantile plot

Quantile-quantile plot with patch option

Impulse response acoustic information calculator

Calculate RT, DRR, Cte, and EDT for impulse response file

Sinc filter

Apply a near-ideal low- or band-pass filter.

, with 1.0 corresponding to half the sample rate.The filtering is performed by FFT-based convolution of x with the sinc kernel.y = sinc_filter(x,Wn,N) allows the filter length to be specified. The default

Subplot position calculator

Calculate subplot positions by specifying figure margins and axis scaling.

Gammatone filterbank

Produce an array of responses from a fourth-order Gammatone filter via FFT

Check whether mex file is compiled for system

Check if mex file is compiled for the OS or if the source was modified since it was compiled.

Modified CMRmap

Produces a colour colormap, of arbitrary length, that is monochrome-compatible.

Statistical Learning Toolbox

Functions for statistical learning, pattern recognition and computer vision, covering many topics.

learning and vision, including classification, regression, statistical modeling, finite mixture model, graph theory-based learning, subspace learning, kernel learning, manifold learning, tensor algebra