Low-Rank and Joint Sparse Representations for Multi-Modal Recognition
We propose multi-task and multivariate methods for multi-modal recognition based on low-rank and joint sparse representations. Our formulations can be viewed as generalized versions of multivariate low-rank and sparse regression, where sparse and low-rank representations across all modalities are im...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 26(2017), 10 vom: 06. Okt., Seite 4741-4752 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | We propose multi-task and multivariate methods for multi-modal recognition based on low-rank and joint sparse representations. Our formulations can be viewed as generalized versions of multivariate low-rank and sparse regression, where sparse and low-rank representations across all modalities are imposed. One of our methods simultaneously couples information within different modalities by enforcing the common low-rank and joint sparse constraints among multi-modal observations. We also modify our formulations by including an occlusion term that is assumed to be sparse. The alternating direction method of multipliers is proposed to efficiently solve the resulting optimization problems. Extensive experiments on three publicly available multi-modal biometrics and object recognition data sets show that our methods compare favorably with other feature-level fusion methods |
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| Beschreibung: | Date Completed 30.07.2018 Date Revised 30.07.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2017.2721838 |