Sparse Multigraph Embedding for Multimodal Feature Representation

• Published in: IEEE transactions on multimedia Vol. 19; no. 7; pp. 1454 - 1466
• Main Authors: Wang, Shiping, Guo, Wenzhong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Clustering; Clustering algorithms; Convergence; Correlation; Data integration; Data sources; Datasets; Factorization; Feature extraction; Feature fusion; graph embedding; Learning systems; Machine learning; multimodal data; Multisensor fusion; Optimization; Sparse matrices; sparse representation; State of the art

Data fusion is used to integrate features from heterogeneous data sources into a consistent and accurate representation for certain learning tasks. As an effective technique for data fusion, unsupervised multimodal feature representation aims to learn discriminative features, indicating the improvem...