Topic Model Kernel Classification with Probabilistically Reduced Features

Journal of Data Science

article

Vu Nguyen¹  Dinh Phung¹  Svetha Venkatesh¹ 

[1] Centre for Pattern Recognition and Data Analytics ,(PRaDA) Deakin University

关键词: Topic Models;    Bayesian Nonparametric;    Support Vector Machine;    Kernel Method;    Classification;    Dimensionality Reduction;   

DOI  :  10.6339/JDS.201504_13(2).0006

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types.

【 授权许可】

CC BY