Bayesian multiview dimensionality reduction for learning predictive subspaces
21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Çek Cumhuriyeti, 18 - 22 Ağustos 2014, cilt.263, ss.387-392, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 263
- Doi Numarası: 10.3233/978-1-61499-419-0-387
- Basıldığı Şehir: Prague
- Basıldığı Ülke: Çek Cumhuriyeti
- Sayfa Sayıları: ss.387-392
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Multiview learning basically tries to exploit different feature representations to obtain better learners. For example, in video and image recognition problems, there are many possible feature representations such as color- and texture-based features. There are two common ways of exploiting multiple views: forcing similarity (i) in predictions and (ii) in latent subspace. In this paper, we introduce a novel Bayesian multiview dimensionality reduction method coupled with supervised learning to find predictive subspaces and its inference details. Experiments show that our proposed method obtains very good results on image recognition tasks in terms of classification and retrieval performances.