A Theoretical Analysis of Multi-Modal Representation Learning with Regular Functions

Multi-modal data analysis methods often learn representations that align different modalities in a new common domain, while preserving the within-class compactness and within-modality geometry and enhancing the between-class separation. In this study, we present a theoretical performance analysis for multi-modal representation learning methods. We consider a quite general family of algorithms learning a nonlinear embedding of the data space into a new space via regular functions. We derive sufficient conditions on the properties of the embedding so that high multi-modal classification or cross-modal retrieval performance is attained. Our results show that if the Lipschitz constant of the embedding function is kept sufficiently small while increasing the between-class separation, then the probability of correct classification or retrieval approaches 1 at an exponential rate with the number of training samples.
2020 28th Signal Processing and Communications Applications Conference (SIU)


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Citation Formats
E. Vural, “A Theoretical Analysis of Multi-Modal Representation Learning with Regular Functions,” presented at the 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Türkiye, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89407.