LTPDA Toolbox™

Method ao/mve

  MVE: Minimum Volume Ellipsoid estimator
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  DESCRIPTION: Minimum Volume Ellipsoid estimator 
               for robust outlier detection.
 
  CALL:        ao_out = mve(ao_in);
               ao_out = mve(ao_in, pl);
  
  The ao_out is the weighted covariance matrix of the data. Other
  information, like the weighted mean, the volume and the center of 
  the ellipsoid are stored in ao_out.procinfo.
 
  Uses the method described in P. Rousseeuw "Robust Regresion and outlier
  Detection, 1987" in pages 258-261. 
 
  **Also in http://www.kimvdlinde.com/professional/pcamve.html
 
  NK 2013  
 
  Parameters Description
 
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Method Details
Access	public
Defining Class	ao
Sealed	0
Static	0

Parameter Description

Sets for this method …
Default

Default
no description
Key	Default Value	Options	Description
mve
PCA	0	0 1	Set to true to perform Principal Component Analysis.
M	100	none	Number 'm' of random sub-samples to be drawn from the data. If set to zero, the method will attempt to proceed taking into acount all possible sub-samples. ATTENTION: If the data-set is too large, this computation is practically unfeasible!
DISCARD	0	none	Discard the first number of samples.

Example
plist('PCA', [false], 'M', [100], 'DISCARD', [0])

Some information of the method ao/mve are listed below:
Class name	ao
Method name	mve
Category	Signal Processing
Package name	ltpda
VCS Version	967b0eec0dece803a81af8ef54ad2f8c784b20b2
Min input args	1
Max input args	-1
Min output args	1
Max output args	-1
Can be used as modifier	1
Supported numeric types	{'double'}

Method: ao/modelSelect

Method: ao/noisegen1D

Method ao/mve

Parameter Description

Default

Example