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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jae-Kuk Lee Kyung-Hun Kim Tae-Young Kim Won-Ho Choi |
| Copyright Year | 2003 |
| Description | Author affiliation: Sch. of Electr. Eng., Ulsan Univ., South Korea (Jae-Kuk Lee; Kyung-Hun Kim; Tae-Young Kim; Won-Ho Choi) |
| Abstract | Principle component analysis (PCA) is a dimensionality reduction technique for data analysis and processing, and it is a linear method that can maximize the variance of data. In this paper, to achieve the high efficiency for data classification, nonlinear PCA using local probability is proposed. Parameters are extracted from each distribution of data and mapping function of data set is made using the relation of the extracted parameters. Nonlinear PCA is performed in new projection feature space. The experimental result is conducted to verify its efficiency compared with the classical linear PCA. |
| Starting Page | 103 |
| Ending Page | 107 |
| File Size | 201534 |
| Page Count | 5 |
| File Format | |
| ISBN | 8978686176 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-06-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | 7th Korea-Russia Intl Sym on Science |
| Subject Keyword | Principal component analysis Equations Data analysis Data mining Covariance matrix Density functional theory Analysis of variance Probability Monitoring Eigenvalues and eigenfunctions |
| Content Type | Text |
| Resource Type | Article |
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