Sparse Tikhonov-Regularized Hashing for Multi-Modal Learning

This paper mainly focuses on the role of regularization in Multi-Modal Learning (MML). Existing MML studies devote most of the efforts in maximizing the consensus of models from cues of different modalities. However, regularization methods are still far from fully explored. To fill in this gap, we propose a compact and efficient coding solution, termed by sparse Tikhonov-Regularized Hashing (STRH). The STRH enforces both the ℓ₀-norm induced sparsity constraints and the Tikhonov regularization on the binary solution vectors which maximize cross-modal correlation. In addition, we raise the concerns on the challenging testing scenario of `Multi-modal Learning and Single-modal Prediction' (MLSP). Finally, we demonstrate that the STRH is an efficient hashing solutions by showing its superiority under the MLSP scenario.