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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lei Ding Xiaole Bai |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH (Lei Ding; Xiaole Bai) |
| Abstract | Regression or least squares fitting is an important problem in statistics, data mining and many other applications. In recent years, basis functions derived from the underlying geometry of data, primarily Laplacian eigenfunctions, have attracted much interest. In this paper, we present a new framework based on adaptive Laplacian eigenfunctions and show the benefit of using a time-varying basis in regression analysis. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 155776 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761298 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Laplace equations Eigenvalues and eigenfunctions Regression analysis Geometry Least squares methods Computer science Data engineering Statistical analysis Data mining Application software |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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