Multi-View Discriminant Analysis
Chinese Academy of Sciences · Institute of Computing Technology · +1 more institution
Abstract
In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the…
Citation impact
- FWCI
- 29.95
- Percentile
- 100%
- References
- 51
Authors
5Topics & keywords
- Artificial intelligence
- Facial recognition system
- Pattern recognition (psychology)
- Computer science
- Linear discriminant analysis
- Cognitive neuroscience of visual object recognition
- Pairwise comparison
- Face (sociological concept)
- Reduced inequalities