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Principal Component Analysis Based on Nonparametric Maximum Entropy 1.0.0
by openpr_nlpr - December 2, 2011, 05:45:02 CET [
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- Description:
In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2-norm or L1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Supported Operating Systems: Windows
- Data Formats: None
- Tags: Pattern Recognition
- Archive: download here
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Project details for Principal Component Analysis Based on Nonparametric Maximum Entropy
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Details
- Version:
- 1.0.0
- Submitter:
- openpr_nlpr
- Author(s):
- Ran He
- Posted:
- 2 December 2011
- Last Updated:
- 2 December 2011
- Open Source License:
- bsd
- Programming Language(s):
- scilab