1. Feature Extraction (I)
Data Mining II Year Lluís Belanche Alfredo Vellido

2. Dimensionality reduction (1)

3. Dimensionality reduction (2)

4. Signal representation vs classification

5. Principal Components Analysis (PCA)
General goal : project the data onto a new subspace so that a maximum of relevant information is preserved
In PCA, relevant information is variance (dispersion).

6. PCA Theory (1)

7. PCA Theory (2)

8. PCA Theory (3)

9. PCA Theory (4)

10. Algorithm for PCA

11. PCA examples (1)

12. PCA examples (2)

13. PCA examples (2)

14. PCA examples (3)

15. PCA examples (4)

16. Two solutions: in which sense are they optimal?
In the signal representation sense
In the signal separation sense
In both
In none

17. Other approaches to FE Kernel PCA: perform PCA in xΦ(x),
where K(x,y) = < Φ(x), Φ(y)> is a kernel
ICA (Independent Components Analysis):
Seeks statistical independence of features (PCA seeks uncorrelated features)
Equivalence to PCA iff features are Gaussian
Image and audio analysis brings own methods
Series expansion descriptors (from the DFT, DCT or DST)
Moment-based features
Spectral features
Wavelet descriptors
Cao, J.J. et al. A comparison of PCA, KPCA and ICA for dimensionality reduction. Neurocomputing 55, pp (2003)