Dimension reduction projects and source code

Ultra math wizard gold in c

The following C project contains the C source code and C examples used for ultra math wizard gold. Hey guys , please vote for this one. This program can create any math c code using a few details from user. Create many physics and maths application in matter of seconds. The Gold Wizard is here.

Mrmr feature selection (using mutual information computation) in matlab

The following Matlab project contains the source code and Matlab examples used for mrmr feature selection (using mutual information computation). This package is the mRMR (minimum-redundancy maximum-relevancy) feature selection method in (Peng et al, 2005 and Ding & Peng, 2005, 2003), whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications.

Minimum redundancy maximum relevance feature selection in matlab

The following Matlab project contains the source code and Matlab examples used for minimum redundancy maximum relevance feature selection. Two source code files of the mRMR (minimum-redundancy maximum-relevancy) feature selection method in (Peng et al, 2005 and Ding & Peng, 2005, 2003), whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications.

Low rank multivariate autoregressive model for dimensionality reduction in matlab

The following Matlab project contains the source code and Matlab examples used for low rank multivariate autoregressive model for dimensionality reduction. Despite the fact that they do not consider the temporal nature of data, classic dimensionality reduction techniques, such as PCA, are widely applied to time series data.

Recovery of low rank and sparse matrix in matlab

The following Matlab project contains the source code and Matlab examples used for recovery of low rank and sparse matrix. This code solves the problem of recovering a low rank and sparse(in transform domain)matrix from its lower dimensional projections Minimize (lambda1)||X||* + (lambda2)||Dx||_1 + 1/2 || A(X) - y ||_2^2 Formulated as an unconstarined nuclear norm and L1 minimization problem using Split bregman algorithm, formulation for the problem is as follows

Efficient multidimensional scaling (mds) in matlab

The following Matlab project contains the source code and Matlab examples used for efficient multidimensional scaling (mds). Quan Wang, Kim L. Boyer. Feature Learning by Multidimensional Scaling and its Applications in Object Recognition. 2013 26th SIBGRAPI Conference on Graphics, Patterns and Images (Sibgrapi). IEEE, 2013. Project wiki: https://sites.google.com/site/mdsfeature/ C++ implementation of MDS: https://sites.google.com/site/simpmatrix/

Independent Component Analysis Matlab Code

Independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.

Feature selection using matlab

The following Matlab project contains the source code and Matlab examples used for feature selection using matlab. The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Sequential Floating Backward Selection (SFBS) • ReliefF Two CCR estimation methods: • Cross-validation • Resubstitution After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.

Rafisher2cda in matlab

The following Matlab project contains the source code and Matlab examples used for rafisher2cda. Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation called canonical discriminant analysis.

Factor analysis by the principal components method without data. in matlab

The following Matlab project contains the source code and Matlab examples used for factor analysis by the principal components method without data. . This m-file deals with the principal component solution of the factor model thru the R-correlation matrix (without the matrix of data; one can also input a covariance matrix), the latent root criterion, and uses the varimax factor rotation.

Directional discrete cosine transform and principal component analysis based image fusion in matlab

The following Matlab project contains the source code and Matlab examples used for directional discrete cosine transform and principal component analysis based image fusion. Image fusion algorithm based on DDCT and PCA is demonstrated. The reference is: VPS Naidu, "Hybrid DDCT-PCA base multi sensor image fusion”, Journal of Optics, Vol. 43, No.1, pp.48-61, March 2014.

Information theoretic feature selection in matlab

The following Matlab project contains the source code and Matlab examples used for information theoretic feature selection. Description: Code (Matlab/C++ Mex) for the following MI based feature selection approaches: - Maximum relevance (maxRel) - Minimum redundancy maximum relevance (MRMR) - Minimum redundancy (minRed) - Quadratic programming feature selection (QPFS) - Mutual information quotient (MIQ) - Maximum relevance minimum total redundancy (MRMTR) or extended MRMR (EMRMR) - Spectral relaxation global Conditional Mutual Information (SPEC_CMI)

Pca Matlab Code

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Principal component analysis

Orthogonal linear regression in 3d space by using principal components analysis in matlab

The following Matlab project contains the source code and Matlab examples used for orthogonal linear regression in 3d space by using principal components analysis. Orthogonal Linear Regression in 3D-space by using Principal Components Analysis This is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components Analysis" (http://www.

Pca (principial component analysis) in matlab

The following Matlab project contains the source code and Matlab examples used for pca (principial component analysis).  - Subtracting the mean of the data from the original dataset  - Finding the covariance matrix of the dataset  - Finding the eigenvector(s) associated with the greatest eigenvalue(s)  - Projecting the original dataset on the eigenvector(s)  - Use only a certain number of the eigenvector(s)  - Do back-project to the original basis vectors Implementation of http://www.

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