Sunday, March 5, 2017

Andrew ng clustering & PCA

randomly assign clusters
assign clusters to each instance
re compute clusters
example image compression - choose R,G,B as numerical features and
assign clusters to each point

example - data compression - reduce n-dimensions to K-dimensions
co variance matrix to capture non axis aligned features' variance(spread)
reconstruct original data by same matrix - U
eigen vectors
example - image compression - choose each pixel as feature - select K
most important ones

scatter3 in octave for 3D-visualization

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