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Techniques for Dimensionality Reduction

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Presentation on theme: "Techniques for Dimensionality Reduction"— Presentation transcript:

1 Techniques for Dimensionality Reduction
Latent Dirichlet Allocation

2 Quick Recap….

3 V[i,j] = pixel j in image i
PCA as MF PC1 10,000 pixels 1000 * 10,000,00 2 prototypes ~ x1 y1 x2 y2 .. … xn yn a1 a2 .. … am b1 b2 bm v11 … vij 1000 images … vnm PC2 V[i,j] = pixel j in image i

4 V[i,j] = pixel j in image i
PC1 1.4*PC *PC2 = 10,000 pixels 1000 * 10,000,00 2 prototypes 1.4 0.5 x2 y2 .. … xn yn a1 a2 .. … am b1 b2 bm v11 … vij 1000 images … vnm PC2 V[i,j] = pixel j in image i

5 PCA for movie recommendation…
m movies m movies ~ x1 y1 x2 y2 .. … xn yn a1 a2 .. … am b1 b2 bm v11 … vij V n users Bob … vnm V[i,j] = user i’s rating of movie j

6 indicators for r clusters
….. vs k-means indicators for r clusters cluster means original data set ~ M 1 .. … xn yn a1 a2 .. … am b1 b2 bm v11 … vij Z X n examples … vnm

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