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Poster
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou · Changyou Chen · Jinhui Xu

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1742

Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in some high-dimensional space, and usually lack the flexibility of being directly applicable to down-stream applications. In this paper, we propose the concept of implicit manifold learning, where manifold information is implicitly obtained by learning the associated heat kernel. A heat kernel is the solution of the corresponding heat equation, which describes how ``heat'' transfers on the manifold, thus containing ample geometric information of the manifold. We provide both practical algorithm and theoretical analysis of our framework. The learned heat kernel can be applied to various kernel-based machine learning models, including deep generative models (DGM) for data generation and Stein Variational Gradient Descent for Bayesian inference. Extensive experiments show that our framework can achieve the state-of-the-art results compared to existing methods for the two tasks.

Author Information

Yufan Zhou (University at Buffalo)
Changyou Chen (University at Buffalo)
Jinhui Xu (SUNY at Buffalo)

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