@Reference(authors="Hans-Peter Kriegel, Peer Kr\u00f6ger, Erich Schubert, Arthur Zimek",title="Outlier Detection in Arbitrarily Oriented Subspaces",booktitle="Proc. IEEE Int. Conf. on Data Mining (ICDM 2012)",url="https://doi.org/10.1109/ICDM.2012.21",bibkey="DBLP:conf/icdm/KriegelKSZ12") @Reference(title="Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography",authors="M. A. Fischler, R. C. Bolles",booktitle="Communications of the ACM 24(6)",url="https://doi.org/10.1145/358669.358692",bibkey="DBLP:journals/cacm/FischlerB81") public class RANSACCovarianceMatrixBuilder extends java.lang.Object implements CovarianceMatrixBuilder
This is an experimental adoption of RANSAC to this problem, not a generic RANSAC implementation!
While using RANSAC for PCA at first sounds like a good idea, it does not work very well in high-dimensional spaces. The problem is that PCA has O(n²) degrees of freedom, so we need to sample very many objects, then perform an O(n³) matrix operation to compute PCA, for each attempt.
RANSAC for PCA was a side note in:
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
In: Proc. IEEE International Conference on Data Mining (ICDM 2012)
The basic RANSAC idea was explained in:
Random sample consensus: a paradigm for model fitting with applications to
image analysis and automated cartography
M. A. Fischler, R. C. Bolles
Communications of the ACM 24(6)
|Modifier and Type||Class and Description|
|Modifier and Type||Field and Description|
Number of iterations to perform
|Constructor and Description|
|Modifier and Type||Method and Description|
Compute Covariance Matrix for a collection of database IDs.
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public RANSACCovarianceMatrixBuilder(int iterations, RandomFactory rnd)
iterations- Number of iterations (attempts) to try
rnd- random generator
public double processIds(DBIDs ids, Relation<? extends NumberVector> relation)
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