2011 ConditionalTopicalCodingAnEffic

• (Zhu et al., 2011) ⇒ Jun Zhu, Ni Lao, Ning Chen, and Eric P. Xing. (2011). “Conditional Topical Coding: An Efficient Topic Model Conditioned on Rich Features.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020484

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Conditional+Topical+Coding%3A+An+Efficient+Topic+Model+Conditioned+on+Rich+Features
• http://dl.acm.org/citation.cfm?id=2020408.2020484&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; conditional models; experimentation; sparse coding; statistical; topic models

Abstract

Probabilistic topic models have shown remarkable success in many application domains. However, a probabilistic conditional topic model can be extremely inefficient when considering a rich set of features because it needs to define a normalized distribution, which usually involves a hard-to-compute partition function. This paper presents conditional topical coding (CTC), a novel formulation of conditional topic models which is non-probabilistic. CTC relaxes the normalization constraints as in probabilistic models and learns non-negative document codes and word codes. CTC does not need to define a normalized distribution and can efficiently incorporate a rich set of features for improved topic discovery and prediction tasks. Moreover, CTC can directly control the sparsity of inferred representations by using appropriate regularization. We develop an efficient and easy-to-implement coordinate descent learning algorithm, of which each coding substep has a closed-form solution. Finally, we demonstrate the advantages of CTC on online review analysis datasets. Our results show that conditional topical coding can achieve state-of-the-art prediction performance and is much more efficient in training (one order of magnitude faster) and testing (two orders of magnitude faster) than probabilistic conditional topic models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ConditionalTopicalCodingAnEffic	Eric P. Xing Jun Zhu Ni Lao Ning Chen			Conditional Topical Coding: An Efficient Topic Model Conditioned on Rich Features				10.1145/2020408.2020484		2011