mdpp

PURPOSE

Minimum Density Projection Pursuit (MDPP) algorithm

SYNOPSIS

function [optHP, idx, spindex] = mdpp(Data, pars, labels, colours)

DESCRIPTION

Minimum Density Projection Pursuit (MDPP) algorithm
[OPTHP, IDX, SPINDEX] = MDPP(X, PARS, LABELS, COLOURS)

 Inputs:
    (X): Data matrix
    (PARS): Structure containing all parameters of mddc() algorithm
    (LABELS): True clusters; used only for visualisation (optional)
    (COLOURS): Colormap matrix used only for visualisation (optional)

 Output:
    (OPTHP): Minimum density hyperplane (if more than one initial projection vectors
        are used then the one that maximises the splitting criterion pars.split_index())
    (IDX): Binary cluster assignment {-1,1}
    (SPINDEX): Value of splitting index criterion

CROSS-REFERENCE INFORMATION

This function calls:

fixLabels Enforces cluster labels to be in the range 1:K
ifelse Shorthand for ternary operator: if-then-else
isOctave Determines whether the environment is GNU Octave (returns TRUE) or MATLAB (returns FALSE)
pcacomp Returns the principal components of (X) specified in vector (index)
reldepth Relative Depth
f_df_md Function value and derivative for penalised density
f_md Penalised density of optimal hyperplane with normal vector (V)
mdhp MDHP implements Minimum Density Hyperplane (inherits from HP class)

This function is called by:

mddc Minimum Density Divisive Clustering
mdh Minimum Density Hyperplane
mdhp MDHP implements Minimum Density Hyperplane (inherits from HP class)