Topic Models for Unsupervised Cluster Matching

2018 ◽  
Vol 30 (4) ◽  
pp. 786-795 ◽  
Author(s):  
Tomoharu Iwata ◽  
Tsutomu Hirao ◽  
Naonori Ueda
Keyword(s):  
Topic Models ◽  
Unsupervised Cluster ◽  
Cluster Matching

scholarly journals Unsupervised Cluster Matching for Content Model

2020 ◽  
Vol 176 (20) ◽  
pp. 39-41
Author(s):  
E. Suchitha ◽  
N. Venkata ◽  
Prasanta Kumar
Keyword(s):  
Unsupervised Cluster ◽  
Content Model ◽  
Cluster Matching

Robust unsupervised cluster matching for network data

2017 ◽  
Vol 31 (4) ◽  
pp. 1132-1154 ◽  
Author(s):  
Tomoharu Iwata ◽  
Katsuhiko Ishiguro
Keyword(s):  
Network Data ◽  
Unsupervised Cluster ◽  
Cluster Matching

Descending Kernel Track of Static and Dynamic Topic Models in Topic Tracking

2012 ◽  
Vol 23 (5) ◽  
pp. 1100-1119 ◽  
Author(s):  
Yu HONG ◽  
Yu CANG ◽  
Jian-Min YAO ◽  
Guo-Dong ZHOU ◽  
Qiao-Ming ZHU
Keyword(s):  
Topic Models ◽  
Topic Tracking

Supervising topic models with Gaussian processes

2018 ◽  
Vol 77 ◽  
pp. 226-236 ◽  
Author(s):  
Melih Kandemir ◽  
Taygun Kekeç ◽  
Reyyan Yeniterzi
Keyword(s):  
Gaussian Processes ◽  
Topic Models

scholarly journals Symptom Cluster-Matching Antidepressant Treatment: A Case Series Pilot Study

2021 ◽  
Vol 14 (6) ◽  
pp. 526
Author(s):  
Sławomir Murawiec ◽  
Marek Krzystanek
Keyword(s):  
Depressive Symptoms ◽  
Pilot Study ◽  
Antidepressant Treatment ◽  
Case Series ◽  
Symptom Cluster ◽  
Study Results ◽  
Cluster Matching ◽  
The Individual ◽  
Meta Analyses ◽  
Effectiveness Studies

Despite treating depression with antidepressants, their effectiveness is often insufficient. Comparative effectiveness studies and meta-analyses show the effectiveness of antidepressants; however, they do not provide clear indications as to the choice of a specific antidepressant. The rational choice of antidepressants may be based on matching their mechanisms of action to the symptomatic profiles of depression, reflecting the heterogeneity of symptoms in different patients. The authors presented a series of cases of patients diagnosed with depression in whom at least one previous antidepressant treatment was shown to be ineffective before drug targeted symptom cluster-matching treatment (SCMT). The presented pilot study shows for the first time the effectiveness of SCMT in the different clusters of depressive symptoms. All the described patients obtained recovery from depressive symptoms after introducing drug-targeted SCMT. Once validated in clinical trials, SCMT might become an effective and rational method of selecting an antidepressant according to the individual profile of depressive symptoms, the mechanism of their formation, and the mechanism of drug action. Although the study results are preliminary, SCMT can be a way to personalize treatment, increasing the likelihood of improvement even in patients who meet criteria for treatment-resistant depression.

Unsupervised domain adaptation based on cluster matching and Fisher criterion for image classification

2021 ◽  
Vol 91 ◽  
pp. 107041
Author(s):  
Heyou Chang ◽  
Fanlong Zhang ◽  
Shuai Ma ◽  
Guangwei Gao ◽  
Hao Zheng ◽  
...  
Keyword(s):  
Image Classification ◽  
Domain Adaptation ◽  
Fisher Criterion ◽  
Unsupervised Domain Adaptation ◽  
Cluster Matching

scholarly journals Expert-Informed Topic Models for Document Set Discovery

2021 ◽  
pp. 1-20
Author(s):  
Eike Mark Rinke ◽  
Timo Dobbrick ◽  
Charlotte Löb ◽  
Cäcilia Zirn ◽  
Hartmut Wessler
Keyword(s):  
Topic Models
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