meanShift clustering algorithm.
Based on code from Sameer Agarwal <sagarwal-at-cs.ucsd.edu>.
For a broad discussion see:
Y. Cheng, Mean-shift, mode seeking, and clustering, IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol.17, 1995, pp. 790-799
The radius or bandwidth is tied to the 'width' of the distribution and is
data dependent. Note that the data should be normalized first so that
all the dimensions have the same bandwidth. The rate determines how
large the gradient decent steps are. The smaller the rate, the more
iterations are needed for convergence, but the more likely minima are not
overshot. A reasonable value for the rate is .2. Low value of the rate
may require an increase in maxIter. Increase maxIter until convergence
occurs regularly for a given data set (versus the algorithm being cut off
at maxIter).
Note the cluster means M do not refer to the actual mean of the points
that belong to the same cluster, but rather the values to which the
meanShift algorithm converged for each given point (recall that cluster
membership is based on the mean converging to the same value from
different points). Hence M is not the same as C, the centroid of the
points [see kmeans2 for a definition of C].
USAGE
[IDX,M] = meanShift(X, radius, [rate], [maxIter], [minCsize], [blur] )
INPUTS
X - column vector of data - N vectors of dim p (X is Nxp)
radius - the bandwidth (radius of the window)
rate - [] gradient descent proportionality factor in (0,1]
maxIter - [] maximum number of iterations
minCsize - [] min cluster size (smaller clusters get eliminated)
blur - [] if blur then at each iter data is 'blurred', ie the
original data points move (can cause 'incorrect' results)
OUTPUTS
IDX - cluster membership [see kmeans2.m]
M - cluster means [see above]
EXAMPLE
See also MEANSHIFTIM, DEMOCLUSTER
Piotr's Computer Vision Matlab Toolbox Version 2.0
Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]