Difference between revisions of "Principal Component Analysis (PCA)"
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* [[AI Solver]] | * [[AI Solver]] | ||
* [[...find outliers]] | * [[...find outliers]] | ||
* [[Dimensional Reduction Algorithms]] | * [[Dimensional Reduction Algorithms]] | ||
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| + | == Anomaly Detection == | ||
| + | [http://www.youtube.com/results?search_query=Principal+Components+Analysis+PCA+Anomaly+Detection+Outliers YouTube search...] | ||
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* [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] | ||
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] | 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] | ||
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| + | == [[Principal Component Analysis (PCA)]] | ||
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Revision as of 07:49, 29 July 2018
Anomaly Detection
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
== Principal Component Analysis (PCA)
