Difference between revisions of "Principal Component Analysis (PCA)"
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* [[...find outliers]] | * [[...find outliers]] | ||
* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/pca-based-anomaly-detection PCA-Based Anomaly Detection | Microsoft] | * [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/pca-based-anomaly-detection PCA-Based Anomaly Detection | Microsoft] | ||
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| + | PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect [http://www.linkedin.com/pulse/part-2-keep-simple-machine-learning-algorithms-big-dr-dinesh/ Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar] | ||
https://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/tk/2013/07/figures/ttk20130714602.gif | https://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/tk/2013/07/figures/ttk20130714602.gif | ||
Revision as of 20:10, 3 June 2018
PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar
PCA
