Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

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Feature extraction and reduced-order modelling of nitrogen plasma models using principal component analysis

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2018.05.012 ◽

2018 ◽

Vol 115 ◽

pp. 504-514 ◽

Cited By ~ 6

Author(s):

Aurélie Bellemans ◽

Gianmarco Aversano ◽

Axel Coussement ◽

Alessandro Parente

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Principal Component ◽

Component Analysis ◽

Nitrogen Plasma ◽

Reduced Order ◽

Reduced Order Modelling

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Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

Big Data Analytics in Bioinformatics and Healthcare - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-6611-5.ch007 ◽

2015 ◽

pp. 138-162 ◽

Cited By ~ 18

Author(s):

Y-H. Taguchi ◽

Mitsuo Iwadate ◽

Hideaki Umeyama ◽

Yoshiki Murakami ◽

Akira Okamoto

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Principal Component ◽

Component Analysis ◽

Circulating Microrna ◽

Biomarker Identification ◽

Typical Situation ◽

Aberrant Dna Methylation ◽

The Stability ◽

Unsupervised Feature Extraction

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

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Applying Weighted PCA on Multiclass Classification for Intrusion Detection

Privacy, Intrusion Detection and Response ◽

10.4018/978-1-60960-836-1.ch009 ◽

2011 ◽

pp. 220-241

Author(s):

Mohsen Moshki ◽

Mehran Garmehi ◽

Peyman Kabiri

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Intrusion Detection ◽

Principal Component ◽

Component Analysis ◽

Multiclass Classification ◽

Extended Version ◽

Number Of Classes

In this chapter, application of Principal Component Analysis (PCA) and one of its extensions on intrusion detection is investigated. This extended version of PCA is modified to cover an important shortcoming of traditional PCA. In order to evaluate these modifications, it is mathematically proved that these modifications are beneficial and later on a known dataset such as the DARPA99 dataset is used to verify results experimentally. To verify this approach, initially the traditional PCA is used to preprocess the dataset. Later on, using a simple classifier such as KNN, the effectiveness of the multiclass classification is studied. In the reported work, instead of traditional PCA, a revised version of PCA named Weighted PCA (WPCA) will be used for feature extraction. The results from applying the aforementioned method to the DARPA99 dataset show that this approach results in better accuracy than the traditional PCA when a number of features are limited, a number of classes are large, and a population of classes is unbalanced. In some situations WPCA outperforms traditional PCA by more than 1% in accuracy.

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