Scaling Subspace-Driven Approaches Using Information Fusion

Book · Wed Feb 01 04:00:00 EST 2023

DOI:https://doi.org/10.5772/intechopen.109946· OSTI ID:1972569

Ghanem, Sally ^[1]; Krim, Hamid ^[2]

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1. ORNL
2. North Carolina State University

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1972569
• Country of Publication: United States
• Language: English