4 citations · 8 across the 4 of their papers we have counts for
5 papers
A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models
Peixian Chen, Zhourong Chen, Nevin L. Zhang
We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and…
Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis
Zhourong Chen, Nevin L. Zhang, Dit-Yan Yeung +1
We are interested in exploring the possibility and benefits of structure learning for deep models. As the first step, this paper investigates the matter for Restricted Boltzmann Ma…
Latent Tree Models for Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Tengfei Liu +3
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a c…
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon +1
Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition,…
A data-driven method for syndrome type identification and classification in traditional Chinese medicine
Nevin L. Zhang, Chen Fu, Teng Fei Liu +4
Objective: The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrom…