Abstract
In supervised learning probabilistic models are attractive to define discriminative models in a rigid mathematical framework. More recently, prototype approaches, known for compact and efficient models, were defined in a probabilistic setting, but are limited to metric vectorial spaces. Here we propose a generalization of the discriminative probabilistic prototype learning algorithm for arbitrary proximity data, widely applicable to a multitude of data analysis tasks. We extend the algorithm to incorporate adaptive distance measures, kernels and non-metric proximities in a full probabilistic framework.
Original language | English |
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Pages (from-to) | 208-216 |
Journal | Neurocomputing |
Volume | 154 |
Early online date | 17 Dec 2014 |
DOIs | |
Publication status | Published - 22 Apr 2015 |
Keywords
- Prototype learning
- Proximity data
- Probabilistic model
- Fuzzy label
- Matrix learning
- Classification