Kuschel, Maurice | Engineering Papers

DOE OSTI2024

RADEMACHER COMPLEXITY REGULARIZATION FOR CORRELATION-BASED MULTIVIEW REPRESENTATION LEARNING

Deep correlation-based multiview representation learning techniques have become increasingly popular methods for extracting highly correlated representations from multiview data. However, their ability to find highly complex mappings between the views can also lead to overfitting and overly correlated representations. In this work, we propose a regularizer for this specific problem, based on the Rademacher complexity of the DNNs, tailored for multiview correlation maximization. We demonstrate that the proposed regularization leads to less noisy representations in synthetic data and improved performance of downstream tasks in real-world multiview datasets.

Kuschel, Maurice↗

DOE OSTI2023

Geodesic-based relaxation for deep canonical correlation analysis

Deep canonical correlation analysis (DCCA) is often applied to paired data samples from diverse sources to extract meaningful common information. However, when the data sources are heterogeneous, some of the useful information may be complementary but not exactly common. In spite of this fact, existing techniques learn maximally correlated representations from multiple views and are formulated so that they aim to yield identical latent subspaces for each view. This approach is sub-optimal in estimating the true signal subspaces for heterogeneous data sources. We propose a residual relaxation for deep canonical correlation analysis (RDCCA) based on a subspace distance metric, which generalizes the existing problem formulation and extracts representations that are better estimates of the actual, non-identical subspaces. We demonstrate that when using such a relaxation, the learned representations are closer to the true ones and that RDCCA outperforms CCA and DCCA in scenarios with heterogeneous data.

Kuschel, Maurice↗