Smoke Detection Based on a Semi-supervised Clustering Model

2014 ◽  
pp. 291-298 ◽  
Author(s):  
Haiqian He ◽  
Liqun Peng ◽  
Deshun Yang ◽  
Xiaoou Chen
Keyword(s):  
Smoke Detection ◽  
Supervised Clustering ◽  
Clustering Model

A new interactive semi-supervised clustering model for large image database indexing

2014 ◽  
Vol 37 ◽  
pp. 94-106 ◽  
Author(s):  
Hien Phuong Lai ◽  
Muriel Visani ◽  
Alain Boucher ◽  
Jean-Marc Ogier
Keyword(s):  
Image Database ◽  
Supervised Clustering ◽  
Clustering Model ◽  
Database Indexing

scholarly journals Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

2021 ◽  
Author(s):  
Junhao WEN ◽  
Erdem Varol ◽  
Aristeidis Sotiras ◽  
Zhijian Yang ◽  
Ganesh B. Chand ◽  
...  
Keyword(s):  
Control Sample ◽  
Optimization Procedure ◽  
Brain Diseases ◽  
Imaging Data ◽  
Clustering Methods ◽  
Disease Heterogeneity ◽  
Supervised Clustering ◽  
Multi Scale ◽  
Clustering Model ◽  
Disease Subtypes

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity in stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data at a basis of feature sets pre- defined at a fixed scale or scales (e.g, an atlas-based regions of interest). Herein we propose a novel method, Multi-scAle heteroGeneity analysIs and Clustering (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to drive inter-scale-consistent disease subtypes or neuroanatomical dimensions effectively. More importantly, to fill in the gap of understanding under what conditions the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N=4403). We then applied MAGIC to real imaging data of Alzheimers disease (ADNI, N=1728) to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of brain diseases. Taken together, we aim to provide guidelines on when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

Adaptive Fuzzy Clustering Model Based on Internal Connectivity of All Data Points

2010 ◽  
Vol 36 (11) ◽  
pp. 1544-1556 ◽  
Author(s):  
Cheng-Long TANG ◽  
Shi-Gang WANG
Keyword(s):  
Fuzzy Clustering ◽  
Adaptive Fuzzy ◽  
Model Based ◽  
Clustering Model ◽  
Data Points

A Geotemporal Clustering Model for COVID-19 Projection

2020 ◽  
Author(s):  
N. Bora Keskin ◽  
Xu Min ◽  
Jing-Sheng Jeannette Song
Keyword(s):  
Clustering Model

Fire smoke detection based on target-awareness and depthwise convolutions

2021 ◽  
Author(s):  
Yunji Zhao ◽  
Haibo Zhang ◽  
Xinliang Zhang ◽  
Xiangjun Chen
Keyword(s):  
Smoke Detection ◽  
Fire Smoke

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

Sign in / Sign up

Export Citation Format

Share Document