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 |
| PCA |
0 |
|
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. |
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| Some information of the method ao/mve are listed below: |
| Class name |
ao |
| Method name |
mve |
| Category |
Signal Processing |
| Package name |
ltpda |
| VCS Version |
8ab8cbbc4bccf7543491a24448f4aae0b1be1c43 |
| Min input args |
1 |
| Max input args |
-1 |
| Min output args |
1 |
| Max output args |
-1 |
|
Method: ao/modelSelect |
|
Method: ao/noisegen1D |
 |
©LTP Team