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Robust Principal Components analysis

Robust Principal Components Analysis

RPCA is splitting the data in two matrices:

Low Rank Matrix: Describes the main features in the data, noise and outliers removed
Sparse Matrix: Consisting of noise AND signals! Makes it quite easy to detect signals/special causes. Can be used e.g. to investigate life time of engines or servo drives.

Method has in general a higher resolution to find groups than standard PCA, apart from this the Low Rank Matrix is more efficient in Feature Extraction than PCA.

Statistics and Analytics

Re: Robust Principal Components analysis

looking forward

Re: Robust Principal Components analysis - Status changed to: In Development

Robust PCA is In Development for JMP 17, which will be available in fall of 2022.

Re: Robust Principal Components analysis - Status changed to: Delivered

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