Corpus ID: 202760132

CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA

@inproceedings{Hnemrder2019CODECD,
  title={CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA},
  author={Maximilian Archimedes Xaver H{\"u}nem{\"o}rder and Anna Beer and Daniyal Kazempour and T. Seidl},
  booktitle={LWDA},
  year={2019}
}
The coMAD (co-median absolute deviation) is a measure for the joint median of two random variables. Previous experiments have shown that a coMAD-based PCA is more robust towards noise and outliers, yielding eigenvectors which represent linear correlation better than its covariance-based competitors. In this preliminary work we introduce CODEC COrrelations in DEnse Clusters a method for detecting linear correlations in dense clusters utilizing a coMAD-based PCA. The idea of CODEC is intriguingly… Expand

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