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| Line 3: |
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| | * [[AI Solver]] | | * [[AI Solver]] |
| | * [[...find outliers]] | | * [[...find outliers]] |
| | + | * [[Anomaly Detection]] |
| | * [[Dimensional Reduction Algorithms]] | | * [[Dimensional Reduction Algorithms]] |
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| | <youtube>kw9R0nD69OU</youtube> | | <youtube>kw9R0nD69OU</youtube> |
| | <youtube>_UVHneBUBW0</youtube> | | <youtube>_UVHneBUBW0</youtube> |
| − |
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| − | == Anomaly Detection ==
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| − | [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]
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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]
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| − | http://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/tk/2013/07/figures/ttk20130714602.gif
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| − |
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| − | == [[Principal Component Analysis (PCA)]]
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| − |
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| − | <youtube>hxGF7cPvs_c</youtube>
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| − | <youtube>ExoAbXPJ7NQ</youtube>
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| − | <youtube>UEPFCp5WpIY</youtube>
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| − | <youtube>6lc6Oz0k9WA</youtube>
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