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NIPS12

Xiao-Ming Wu, Zhenguo Li, Anthony Man-Cho So, John Wright, Shih-Fu Chang. Learning with Partially Absorbing Random Walks. In Proceedings of Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA, 2012.

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Abstract

We propose a novel stochastic process that is with probability $\alpha_i$ being absorbed at current state $i$, and with probability $1-\alpha_i$ follows a random edge out of it. We analyze its properties and show its potential for exploring graph structures. We prove that under proper absorption rates, a random walk starting from a set $\mathcal{S}$ of low conductance will be mostly absorbed in $\mathcal{S}$. Moreover, the absorption probabilities vary slowly inside $\mathcal{S}$, while dropping sharply outside, thus implementing the desirable cluster assumption for graph-based learning. Remarkably, the partially absorbing process unifies many popular models arising in a variety of contexts, provides new insights into them, and makes it possible for transferring findings from one paradigm to another. Simulation results demonstrate its promising applications in retrieval and classification

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Xiao-Ming Wu
Zhenguo Li
Shih-Fu Chang

BibTex Reference

@InProceedings{NIPS12,
   Author = {Wu, Xiao-Ming and Li, Zhenguo and So, Anthony Man-Cho and Wright, John and Chang, Shih-Fu},
   Title = {Learning with Partially Absorbing Random Walks},
   BookTitle = {Proceedings of Annual Conference on Neural Information Processing Systems (NIPS)},
   Address = {Lake Tahoe, NV, USA},
   Year = {2012}
}

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