Relative Information Loss in the PCA

Details Relative Information Loss in the PCA Relative Information Loss in the PCA

2012-04-02

arxiv.org/abs/1204.0429v1

Computer Science

Information Theory

5 pages, 2 figure; submitted

Scientific paper

In this work we analyze principle component analysis (PCA) as a deterministic input-output system. We show that the relative information loss induced by reducing the dimensionality of the data after performing the PCA is the same as in dimensionality reduction without PCA. Finally, we analyze the case where the PCA uses the sample covariance matrix to compute the rotation. If the rotation matrix is not available at the output, we show that an infinite amount of information is lost. The relative information loss is shown to decrease with increasing sample size.

Affiliated with

Also associated with

No associations

Rating

Relative Information Loss in the PCA does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Relative Information Loss in the PCA, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Relative Information Loss in the PCA will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-509849