Geometric Scattering

Geometric scattering is a mathematical framework extending wavelet-based transforms to analyze data residing on non-Euclidean spaces like graphs and manifolds, mirroring the success of convolutional neural networks on Euclidean data. Current research focuses on developing and refining geometric scattering transforms for various data structures, including the design of learnable filter modules within these transforms to improve performance and efficiency in tasks such as graph classification and clustering. This approach offers improved representation learning for complex data, leading to advancements in geometric deep learning and applications across diverse fields including bioinformatics and image analysis.