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arXiv · 1510.07035

Fast Latent Variable Models for Inference and Visualization on Mobile Devices

Joseph W Robinson·Aaron Q Li
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Abstract

In this project we outline Vedalia, a high performance distributed network for performing inference on latent variable models in the context of Amazon review visualization. We introduce a new model, RLDA, which extends Latent Dirichlet Allocation (LDA) [Blei et al., 2003] for the review space by incorporating auxiliary data available in online reviews to improve modeling while simultaneously remaining compatible with pre-existing fast sampling techniques such as [Yao et al., 2009; Li et al., 2014a] to achieve high performance. The network is designed such that computation is efficiently offloaded to the client devices using the Chital system [Robinson & Li, 2015], improving response times and reducing server costs. The resulting system is able to rapidly compute a large number of specialized latent variable models while requiring minimal server resources.

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Joseph W Robinson, Aaron Q Li. 2015-10-23. Fast Latent Variable Models for Inference and Visualization on Mobile Devices. https://arxiv.org/abs/1510.07035

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