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|Title:||Simultaneous and orthogonal decomposition of data using multimodal discriminant analysis|
|Authors:||Sim, T. |
|Citation:||Sim, T., Zhang, S., Li, J., Chen, Y. (2009). Simultaneous and orthogonal decomposition of data using multimodal discriminant analysis. Proceedings of the IEEE International Conference on Computer Vision : 452-459. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2009.5459189|
|Abstract:||We present Multimodal Discriminant Analysis (MMDA), a novel method for decomposing variations in a dataset into independent factors (modes). For face images, MMDA effectively separates personal identity, illumination and pose into orthogonal subspaces. MMDA is based on maximizing the Fisher Criterion on all modes at the same time, and is therefore well-suited for multimodal and mode-invariant pattern recognition. We also show that MMDA may be used for dimension reduction, and for synthesizing images under novel illumination and even novel personal identity. ©2009 IEEE.|
|Source Title:||Proceedings of the IEEE International Conference on Computer Vision|
|Appears in Collections:||Staff Publications|
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