Multimodal subspace support vector data description
Sohrab, Fahad; Raitoharju, Jenni; Iosifidis, Alexandros; Gabbouj, Moncef (2021)
Sohrab, Fahad
Raitoharju, Jenni
Iosifidis, Alexandros
Gabbouj, Moncef
2021
107648
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202009297162
https://urn.fi/URN:NBN:fi:tuni-202009297162
Kuvaus
Peer reviewed
Tiivistelmä
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.
Kokoelmat
- TUNICRIS-julkaisut [16977]