The k-means algorithm is widely used in a number applications like speech processing and image compression.
This script implements the algorithm in a simple but general way. It performs four basic steps.
1. Define k arbitrary prototypes from the data samples. 2. Assign each sample to the nearest prototype. 3. Recalculate prototypes as arithmetic means. 4. If a prototype changes, repeat step (2).
A GENERALAZED CONVOLUTION COMPUTING CODE IN MATLAB WITHOUT USING MATLAB BUILTIN FUNCTION conv(x,h)
Hard and soft k-means implemented simply in python (with numpy). Quick and dirty, tested and works on large (10k+ observations, 2-10 features) real-world data.
[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X into k (or, rarely, fewer) clusters by applying the well known batch K-means algorithm. Rows of X correspond to points, columns correspond to variables. The...
This is a very user friendly Gram Schmidth Algorithm implemented in MATLAB. I have already submited a file of the same algo,bt this one is bit more flexible than previous. Hope u will find it useful.
We run ROI_extract code in Matlab, and chose the image which want to extract ROI from it and we select the ROI.
PBKM is simple and effective which can outperform the K-means algorithm.
Genetic algorithm written in Matlab.
Matlab code for the algorithm published in
V. G. Reju, S. N. Koh and I. Y. Soon, Convolution Using Discrete Sine and Cosine Transforms, IEEE Signal Processing Letters, VOL. 14, NO. 7, JULY 2007, pp.445-448.
This package contains the MATLAB code for the robust point-set registration algorithm discribed in the ICCV'05 paper:
"Bing Jian and Baba C. Vemuri, A Robust Algorithm for Point Set Registration Using Mixture of...
This is a tool for K-means clustering. After trying several different ways to program, I got the conclusion that using simple loops to perform distance calculation and comparison is most efficient and accurate because of the JIT acceleration in...
The example is on developing an algorithm for detecting an object (green ball) in MATLAB. The demo highlights * image import (and video import) * image visualization * simple image processing * automatic report generation
Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.
M-files accompanying the " Genetic Algorithms & New Optimization Methods in MATLAB " webinar.
These files provide what you need to run the two demos: Optimization of non-smooth objective function, and Optimization of a...
Example code from "Handling Large Data Sets Efficiently in MATLAB " webinar (http://www.mathworks.com/company/events/we.../wbnr30435.html) describing strategies for handling large amounts of data in MATLAB and avoiding...
k-clique algorithm as defined in the paper "Uncovering the overlapping community structure of complex networks in nature and society" - G. Palla, I. DerdoTenyi, I. Farkas, and T. Vicsek - Nature 435, 814d-deOCt818 (2005)
M-files accompanying the webinar titled "New Approaches to Constrained Optimization in MATLAB" held on November 05, 2005
These files provide what you need to run the two demos: 1) The first demo is a demonstration of...
1. Introduction
Enough debate has been devoted from time to time to the luck of "go to" in Matlab. Ourselves being somewhat devoted followers of structured programming, we rarely use it in our FORTRAN applications...
Most of the kdtree code for matlab has been implemented via mex files. I decided to come up with a purely matlab based implementation and so here it is .... The code is obviously expected to be slower than some of the c/c++ implementations that...
Explain why we use fftshift(fft(fftshift(x))) in Matlab instead of fft(x). An example is given. The example and Matlab codes are partially copied from Daniele Disco d-de?s work in "A guide to the Fast Fourier Transform, 2nd Edition". |