Abstract
Linear discriminant analysis (LDA) is suboptimal in dealing with multimodal data that multiple clusters per class exist in input space. This is caused by its inherent globality. To attack this problem, a novel extension of LDA is presented which is called cluster-based correlation discriminative analysis (CCDA). CCDA encodes correlation-based similarity metric in cluster structure modeling, aiming to preserve the correlational affinity in lower-dimensional subpace. Extensive experiments on two widely used databases validate that CCDA outperforms existing LDA variants in facial expression recognition tasks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
