Manifold Integration with Markov Random Walks

Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors at a time so that each sensory information yields different similarity matrix derived from the same objects. In such a case, manifold integration is a desirable task, combining these similarity matrices into a compromise matrix that faithfully reflects multiple sensory information. A small number of methods exists for manifold integration, including a method based on reproducing kernel Krein space (RKKS) or DISTATIS, where the former is restricted to the case of only two manifolds and the latter considers a linear combination of normalized similarity matrices as a compromise matrix. In this paper we present a new manifold integration method, Markov random walk on multiple manifolds (RAMS), which integrates transition probabilities defined on each manifold to compute a compromise matrix. Numerical experiments...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Heeyoul Choi, Seungjin Choi, Yoonsuck Choe
• Keywords: AAAI 2008, Compromise Matrix, Intelligent Agents, Manifold Integration, Manifolds