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Nonlinear Multiple Kernel Learning via Mixture of Probabilistic Kernel Discriminant Analysis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhaozheng, Zheng Zhao |
| Copyright Year | 2010 |
| Abstract | Multiple kernel learning (MKL) provides a powerful tool for heterogenous data integration. Most existing MKL formulations are based on a linear kernel combination, which, however, restricts the flexibility of the learning model. In this paper, we propose a novel nonlinear multiple kernel learning formulation based on the model combination. The proposed formulation (called MPKDA) is derived from a novel probabilistic model for kernel discriminant analysis (KDA) and its mixture. Experimental results on various real applications demonstrate that the proposed MPKDA model provides competitive performance comparing with the representative approaches. We also analyze the relationship between the proposed model and the existing KDA-based MKL formulations, and show how to use the proposed MPKDA model to handle missing data and perform localized multiple kernel learning (LMKL). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.public.asu.edu/~huanliu/papers/tr-10-008.pdf |
| Alternate Webpage(s) | http://www.public.asu.edu/~zzhao15/papers/TR-10-008%20Zhao,%20Yu,Ye,Liu.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
