1 / 18

COMPE 467 - Pattern Recognition

Dimensionality Reduction. COMPE 467 - Pattern Recognition. Dimensionality Reduction. We can reduce dimensionality by combining features . Principle Component Analysis seeks a projection that best represents the data in a least square sense. 1. Principal Component Analysis. 1.

caelan
Télécharger la présentation

COMPE 467 - Pattern Recognition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dimensionality Reduction COMPE 467 - Pattern Recognition

  2. Dimensionality Reduction • We can reducedimensionalitybycombiningfeatures. • PrincipleComponentAnalysisseeks a projectionthatbestrepresentsthe data in a leastsquare sense. 1

  3. Principal Component Analysis 1

  4. Principal Component Analysis This function is minimized if xo is equal to mean  1

  5. Principal Component Analysis • The sample mean is the zero-degree representation of the entire dataset. • Simple, but provides no information about the variability in the data. • A better representation can be obtained with a projection, a line, through the sample mean 1

  6. Principal Component Analysis 1

  7. Principal Component Analysis 1

  8. Principal Component Analysis 1

  9. Principal Component Analysis 1

  10. Principal Component Analysis 1

  11. Principal Component Analysis 1

  12. Principal Component Analysis 1

  13. Principal Component Analysis 1

  14. PCA - Example HOW ? 1

  15. PCA - Example HOW ? 1

  16. PCA – Example (cont.) m 1

  17. PCA – Example (cont.) Continue and find principle components. 1

  18. References • R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, New York: John Wiley, 2001. • Robi Polikar, “Theory and Applications of Pattern Recognition – Lecture Presentations”, Rowan University, Glassboro, NJ.

More Related