A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

AIChE Journal ◽  
2012 ◽  
Vol 59 (2) ◽  
pp. 407-419 ◽  
Author(s):  
Jie Yu
Keyword(s):  
Fault Detection ◽  
Probabilistic Method ◽  
Chemical Processes ◽  
Unlabeled Data ◽  
Support Vector ◽  
Complex Chemical ◽  
Support Vector Clustering ◽  
Fault Detection And Classification ◽  
Vector Clustering

Pinball Loss Twin Support Vector Clustering

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-23
Author(s):  
M. Tanveer ◽  
Tarun Gupta ◽  
Miten Shah ◽  
Keyword(s):  
Loss Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Structural Mri ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Support Vector Clustering ◽  
Hinge Loss ◽  
Pinball Loss ◽  
Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

scholarly journals MPMSVC: Multiple Parametric-Margin Support Vector Clustering

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 24499-24512
Author(s):  
Yi-Bo Jiang ◽  
Wei-Jie Chen ◽  
Yu-Qing Wang ◽  
Ming-Chuan Zhang ◽  
Yuan-Hai Shao
Keyword(s):  
Support Vector ◽  
Support Vector Clustering ◽  
Vector Clustering
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