Hybrid linear matrix factorization for topic-coherent terms clustering

作者：

Highlights：

• We propose a novel Karhunen–Loève Transformation (KLT) for dimension reduction.

• Karhunen–Loève expansion based on Wiener process on KLT results for optimization.

• State-of-the-art topic-coherence metrics are used for word clustering and evaluation.

摘要

•We propose a novel Karhunen–Loève Transformation (KLT) for dimension reduction.•Karhunen–Loève expansion based on Wiener process on KLT results for optimization.•State-of-the-art topic-coherence metrics are used for word clustering and evaluation.

论文关键词：Matrix factorization,Dimensional reduction,Term clustering,Karhunen–Loève transformation

论文评审过程：Received 25 February 2016, Revised 20 May 2016, Accepted 12 June 2016, Available online 18 June 2016, Version of Record 28 June 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.06.022