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Blind Source Sepearte GUI
4.0
Application BSSGUI can be used as a control panel (graphical user interface) to several algorithms of Blind Source Separation: EFICA, WASOBI, COMBI, MULTI-COMBI, FCOMBI, including adjusting characteristics and parametres of all of them.
It enables to work with input and output data, multiple signal plot and saving output variables to the base Matlab workspace
wangxiaolong0312 2 0
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PCA and ICA Package
no vote
This package contains functions that implement Principal Component Analysis (PCA) and its lesser known cousin, Independent Component Analysis (ICA).
PCA and ICA are implemented as functions in this package, and multiple examples are included to demonstrate their use.
In PCA, multi-dimensional data is projected onto the singular vectors corresponding to a few of its largest singular values. Such an operation effectively decomposes the input single into orthogonal components in the directions of largest variance in the data. As a result, PCA is often used in dimensionality reduction applications, where performing PCA yields a low-dimensional representation of data that can be reversed to closely reconstruct the original data.
In ICA, multi-dimensional data is decomposed into components that are maximally independent in the negentropy sense. ICA differs from PCA in that the low-dimensional signals do not necessarily correspond to the directions
wangxiaolong0312 1 0
1
Mixing matrix estimation in instantaneous blind so
4.0
This program will estimate the single source points present in the
instantaneous mixtures and using the estimated single source points the mixing matrix will be estimated. Then the error in mixing matrix
estimation and the latest estimated mixing matrix will be returned.
Here the hierarchical clustering algorithm is used to cluster the
estimated single source points. It is not necessary to use hierarchical clustering algorithm, instead any other suitable clustering algorithm can be used.
wangxiaolong0312 0 0
1
Total Least Squares Method
3.0
a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of:
- linear regression model
- nonlinear regression model
- fitting data in 3D space
- identification of dynamical system
wangxiaolong0312 1 0
1
PCA Based Face Recognition System Using ORL Databa
4.0
This package implements a well-known PCA-based face recognition method, which is called 'Eigenface'.
The program is easy to use. Furthermore, a sample Project file 'ProjectPCA.m' is added that demonstrate how to use, ORL training and test database is also included to show Performance comparison for execution time and Recognition percentage, on different size of testing and training dataset.
wangxiaolong0312 3 0
1
Shuffled Complex Evolution with PCA (SP-UCI) method
no vote
The shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI) method is a global optimization algorithm designed for high-dimensional and complex problems. It is based on the Shuffled Complex Evolution (SCE-UA) Method (by Dr. Qingyun Duan et al.), but solves a serious problem in searching over high-dimensional spaces," population degeneration". The population degeneration problem refers to the phenomenon that, when searching over the highdimensional parameter spaces, the population of the searching particles is very likely to collapse into a subspace of the parameter space, therefore losing the capability of exploring the entire parameter space. In addition, the SP-UCI method also combines the strength of shuffled complex, the Nelder-Mead simplex, and mutinormal resampling to achieve efficient and effective high-dimensional optimization.
wangxiaolong0312 0 0
1
Kernel PCA and Pre-Image Reconstruction
no vote
Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.
wangxiaolong0312 0 0
1
thumbnailScatter.m puts thumbnails over datapoints for better visualization
no vote
It is much easier to spot patterns in your data when you can associate an image with each datapoint.
thumbnailScatter.m overlays an image thumbnail over each datapoint to help with visualization.
For example, if you perform a principal components analysis and would like to plot PC1 vs PC2, use thumbnailScatter.m to see what properties each PC controls.
wangxiaolong0312 0 0
1
Empirical orthogonal function (PCA) estimation for EEG time series
no vote
This source contains the empirical orthogonal functional analysis (EOF) calculation for an individual or population of EEG power spectrum multivariate time series. The zip file contains several versions of the code useful for different contexts, including code that returns only the first EOF versus code that returns all N EOFs
wangxiaolong0312 0 0
1
cellular signals from imaging data
no vote
This toolbox includes routines for using principal component analysis (PCA) and independent component analysis (ICA) to extract cellular signals from imaging data sets. A full description and validation of the method is provided in the paper, "Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data," Neuron, 63:747 (2009)
wangxiaolong0312 0 0
1
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