A Gyro Attention Framework for Matrix Manifolds

We introduce an iterative framework to jointly learn hierarchical representations of both samples and features using tree-Wasserstein distance and data-driven Haar wavelet filters.

Guided manifold learning and semi-supervised visualization with natural out-of-sample extension based on random forest proximities and diffusion geometry-regularized autoencoder architecture.