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Multi-view linear discriminant analysis network
journal contribution
posted on 2019-11-01, 00:00 authored by P Hu, D Peng, Y Sang, Yong XiangYong XiangIn many real-world applications, an object can be described from multiple views or styles, leading to the emerging multi-view analysis. To eliminate the complicated (usually highly nonlinear) view discrepancy for favorable cross-view recognition and retrieval, we propose a Multi-view Linear Discriminant Analysis Network (MvLDAN) by seeking a nonlinear discriminant and view-invariant representation shared among multiple views. Unlike existing multi-view methods which directly learn a common space to reduce the view gap, our MvLDAN employs multiple feedforward neural networks (one for each view) and a novel eigenvalue-based multi-view objective function to encapsulate as much discriminative variance as possible into all the available common feature dimensions. With the proposed objective function, the MvLDAN could produce representations possessing: 1) low variance within the same class regardless of view discrepancy, 2) high variance between different classes regardless of view discrepancy, and 3) high covariance between any two views. In brief, in the learned multi-view space, the obtained deep features can be projected into a latent common space in which the samples from the same class are as close to each other as possible (even though they are from different views), and the samples from different classes are as far from each other as possible (even though they are from the same view). The effectiveness of the proposed method is verified by extensive experiments carried out on five databases, in comparison with the 19 state-of-the-art approaches.
History
Journal
IEEE transactions on image processingVolume
28Issue
11Pagination
5352 - 5365Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
1057-7149eISSN
1941-0042Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, IEEEUsage metrics
Keywords
Linear discriminant analysisImage reconstructionNeural networksKernelFeature extractionLInear programmingCorrelationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringHeterogeneous recognitioncross-view retrievallatent common spacemulti-view linear discriminant analysis networkmulti-view representation learningRECOGNITIONArtificial Intelligence and Image Processing
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