Geometric Multimodal Deep Learning With Multiscaled Graph Wavelet Convolutional Network.

Area of Science:

• Machine Learning
• Data Science
• Graph Signal Processing

Background:

• Multimodal data offer complementary insights into phenomena by integrating diverse data types.
• Existing multimodal learning methods struggle to capture both intramodality and cross-modality information effectively.
• Geometry-aware approaches and deep learning on non-Euclidean domains are emerging areas for data analysis.

Purpose of the Study:

• To propose an end-to-end network, the multimodal graph wavelet convolutional network (M-GWCN), for comprehensive multimodal data analysis.
• To develop a method capable of capturing intramodality and cross-modality information without requiring homogeneous data or prior correspondence knowledge.
• To generalize deep learning to non-Euclidean domains for multimodal data representation.

Main Methods:

• The proposed M-GWCN utilizes multiscale graph wavelet transform for intramodality representation, providing localization in the graph domain.
• Cross-modality representation is achieved by learning permutations that encode correlations between different data modalities.
• The network is designed to handle heterogeneous modalities and does not require prior knowledge of correspondences.

Main Results:

• Experiments on unimodal and multimodal datasets demonstrate the superiority of M-GWCN.
• The method effectively captures intramodality and cross-modality information, outperforming spectral graph convolutional networks.
• M-GWCN shows effectiveness in semisupervised node classification tasks on diverse datasets.

Conclusions:

• The M-GWCN provides an effective end-to-end solution for multimodal data analysis by integrating geometry-aware principles.
• The proposed network advances multimodal learning by handling heterogeneous data and learning cross-modality correlations.
• M-GWCN represents a significant step in generalizing deep learning to complex, non-Euclidean multimodal data structures.