Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 2014 |
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<mark>Journal</mark> | Proceedings of Machine Learning Research |
Volume | 33 |
Number of pages | 10 |
Pages (from-to) | 47-56 |
Publication Status | Published |
<mark>Original language</mark> | English |
Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels.