scholarly journals A New One-class Classifier: Relevant Component Analysis Data Description

2012 ◽  
Vol 33 ◽  
pp. 899-904
Author(s):  
Zhe Wang ◽  
Daqi Gao
Keyword(s):  
Analysis Data ◽  
Component Analysis ◽  
Data Description ◽  
One Class Classifier ◽  
Relevant Component

Adjustment Learning and Relevant Component Analysis

2002 ◽  
pp. 776-790 ◽  
Author(s):  
Noam Shental ◽  
Tomer Hertz ◽  
Daphna Weinshall ◽  
Misha Pavel
Keyword(s):  
Component Analysis ◽  
Relevant Component

scholarly journals Principal component analysis of conformation traits in Hungarian Simmental cows

2021 ◽  
Vol 66 (No. 2) ◽  
pp. 39-45
Author(s):  
Evelin Török ◽  
István Komlósi ◽  
Béla Béri ◽  
Imre Füller ◽  
Barnabás Vágó ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Analysis Data ◽  
Component Analysis ◽  
Linear Type ◽  
Dual Purpose ◽  
Type Traits ◽  
And Cluster Analysis ◽  
Trait Groups

The aim of the current research was to analyze the linear type traits of Hungarian Simmental dual-purpose cows scored in the first lactation using principal component analysis and cluster analysis. Data collected by the Association of Hungarian Simmental Breeders were studied during the work. The filtered database contained the results of 8 868 cows, born after 1997. From the evaluation of main conformation traits, the highest correlations (r = 0.35, P < 0.05) were found between mammary system and feet and legs traits. Within linear type traits, the highest correlation was observed between rump length and rump width (r = 0.81, P < 0.05). Using the principal component analysis, main conformation traits were combined into groups. There were three factors having 84.5 as total variance ratio after varimax rotation. Cluster analysis verified the results of the principal component analysis as most of the trait groups were similar. The strongest relationship was observed between feet and legs and mammary system (main conformation traits) and between rump length and rump width (linear type traits).

scholarly journals An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 648 ◽  
Author(s):  
Mengfei Zhou ◽  
Qiang Zhang ◽  
Yunwen Liu ◽  
Xiaofang Sun ◽  
Yijun Cai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Leak Detection ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Operating Modes

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

scholarly journals Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis

2009 ◽  
Vol 3 (1) ◽  
pp. 148-158 ◽  
Author(s):  
Kevin M. Carter ◽  
Raviv Raich ◽  
William G. Finn ◽  
Alfred O. Hero
Keyword(s):  
Flow Cytometry ◽  
Analysis Data ◽  
Component Analysis ◽  
Flow Cytometry Analysis
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