Techniques for multimodal data fusion having a multimodal hierarchical dictionary learning framework that learns latent subspaces with hierarchical overlaps are provided. In one aspect, a method for multi-view data fusion with hierarchical multi-view dictionary learning is provided which includes the steps of: extracting multi-view features from input data; defining feature groups that group together the multi-view features that are related; defining a hierarchical structure of the feature groups; and learning a dictionary using the feature groups and the hierarchy of the feature groups. A system for multi-view data fusion with hierarchical multi-view dictionary learning is also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for multi-view data fusion with hierarchical multi-view dictionary learning, the method comprising the steps of: extracting multi-view features from input data; defining feature groups that group together the multi-view features that are related; defining a hierarchical structure of the feature groups; and learning a dictionary using the feature groups and the hierarchy of the feature groups. 2. The method of claim 1, further comprising the step of: using the dictionary as a sparse representation of the input data to perform analytic tasks. 3. The method of claim 2, further comprising the step of: using the dictionary as input to a multimodal human emotion recognition system. 4. The method of claim 2, further comprising the step of: refining the feature groups and the hierarchical structure of the feature groups based on feedback from the analytic tasks performed using the dictionary. 5. The method of claim 1, wherein the input data comprises a set of signals collected from multiple modalities. 6. The method of claim 1, wherein the feature groups are defined using domain knowledge. 7. The method of claim 1, wherein the step of defining the feature groups comprises the step of: learning the feature groups from the input data. 8. The method of claim 1, wherein the hierarchical structure of the feature groups is defined using domain knowledge. 9. The method of claim 1, wherein the step of defining the hierarchical structure of the feature groups comprises the step of: learning the hierarchical structure of the feature groups from the input data. 10. The method of claim 9, further comprising the steps of: using the heterogeneous sets of features and the feature groups to generate a power set over the feature groups; and constructing the hierarchical structure of the feature groups following superset-subset relationships among subsets in the power set. 11. The method of claim 1, wherein the step of learning the dictionary using the feature groups and the hierarchy of the feature groups comprises the steps of: using the heterogeneous sets of features, the feature groups, and the hierarchical structure of the feature groups to create an initial dictionary; calculating sparse coefficients using the initial dictionary; assigning subspaces to the hierarchical structure of the feature groups; optimizing the sparse coefficients while keeping the dictionary fixed; optimizing the dictionary while keeping the sparse coefficients fixed; and repeating the steps of assigning the subspaces to the hierarchical structure of the feature groups, optimizing the sparse coefficients, and optimizing the dictionary until convergence 12. The method of claim 11, wherein the step of assigning the subspaces to the hierarchical structure of the feature groups comprises the step of: finding a map between subspace vectors and nodes of the hierarchical structure of the feature groups. 13. A system for multi-view data fusion with hierarchical multi-view dictionary learning, the system comprising: a first group definition module configured to define feature groups from multi-view features extracted from input data, wherein the feature groups group together the multi-view features that are related; a second group definition module configured to define a hierarchical structure of the feature groups; and a dictionary module configured to learn a dictionary using the feature groups and the hierarchy of the feature groups. 14. The system of claim 13, wherein the input data comprises a set of signals collected from multiple modalities. 15. The system of claim 13, wherein the first group definition module is configured to learn the feature groups from the input data. 16. The system of claim 13, wherein the second group definition module is configured to learn the hierarchical structure of the feature groups from the input data. 17. The system of claim 16, wherein the second group definition module is configured to: use the heterogeneous sets of features and the feature groups to generate a power set over the feature groups; and construct the hierarchical structure of the feature groups following superset-subset relationships among subsets in the power set. 18. The system of claim 13, wherein the dictionary module is configured to: use the heterogeneous sets of features, the feature groups, and the hierarchical structure of the feature groups to create an initial dictionary; calculate sparse coefficients using the initial dictionary; assign subspaces to the hierarchical structure of the feature groups; optimize the sparse coefficients while keeping the dictionary fixed; optimize the dictionary while keeping the sparse coefficients fixed; and repeat the steps of assigning the subspaces to the hierarchical structure of the feature groups, optimizing the sparse coefficients, and optimizing the dictionary until convergence. 19. A computer program product for multi-view data fusion with hierarchical multi-view dictionary learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to: extract multi-view features from input data; define feature groups that group together the multi-view features that are related; define a hierarchical structure of the feature groups; and learn a dictionary using the feature groups and the hierarchy of the feature groups. 20. The computer program product of claim 19, wherein the input data comprises a set of signals collected from multiple modalities.