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	Podcast:		Linear Digressions
	Episode:		Facial Recognition with Eigenfaces
	Category:		Technology
	Duration:		00:10:01
	Publish Date:		2015-01-06 19:30:40
	Description:		A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that contain the most variation in the data, and those principal components are often then fed into a classic ML algorithm like and SVM. One of the best thing about eigenfaces is the great example code that you can find in sklearn--you can distinguish pictures of world leaders yourself in just a few minutes! http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html
	Total Play:		2

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