Proceedings ArticleDOI
Feature Selection Using Principal Component Analysis
Fengxi Song,Zhongwei Guo,Dayong Mei +2 more
- Vol. 1, pp 27-30
TLDR
The analysis clearly shows that PCA has the potential to perform feature selection and is able to select a number of important individuals from all the feature components and the devised algorithm is not only subject to the nature of PCA but also computationally efficient.Abstract:
Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection issue, from a viewpoint of numerical analysis. The analysis clearly shows that PCA has the potential to perform feature selection and is able to select a number of important individuals from all the feature components. Our method assumes that different feature components of original samples have different effects on feature extraction result and exploits the eigenvectors of the covariance matrix of PCA to evaluate the significance of each feature component of the original sample. When evaluating the significance of the feature components, the proposed method takes a number of eigenvectors into account. Then it uses a reasonable scheme to perform feature selection. The devised algorithm is not only subject to the nature of PCA but also computationally efficient. The experimental results on face recognition show that when the proposed method is able to greatly reduce the dimensionality of the original samples, it also does not bring the decrease in the recognition accuracy.read more
Citations
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Journal ArticleDOI
Radiomics: the process and the challenges
Virendra Kumar,Yuhua Gu,Satrajit Basu,Anders Berglund,Steven A. Eschrich,Matthew B. Schabath,Kenneth M. Forster,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Andre Dekker,David Fenstermacher,Dmitry B. Goldgof,Lawrence O. Hall,Philippe Lambin,Yoganand Balagurunathan,Robert A. Gatenby,Robert J. Gillies +16 more
TL;DR: "Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging, leading to a very large potential subject pool.
Journal ArticleDOI
A Critical Review of Machine Learning of Energy Materials
TL;DR: In this article, the authors provide an in-depth, critical review of ML-guided design and discovery of energy materials, a field where a novel material with superior performance (e.g., higher energy density, higher energy conversion efficiency, etc.) can have a transformative impact on the urgent global problem of climate change.
Journal ArticleDOI
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.
TL;DR: An overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies is presented, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting.
Journal ArticleDOI
Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring
TL;DR: The intelligent classification methods, support vector machines (SVM) and convolutional neural network (CNN) were proposed for quality level identification in this work and indicated the information from different objects is sensitive to different types of quality anomalies.
Journal ArticleDOI
Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.
Ahmad Chaddad,Michael Jonathan Kucharczyk,Paul Daniel,Siham Sabri,Bertrand J. Jean-Claude,Tamim Niazi,Bassam Abdulkarim,Bassam Abdulkarim +7 more
TL;DR: The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma, as it pertains to high grade gliomas.
References
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Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Journal ArticleDOI
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
Book ChapterDOI
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
TL;DR: A face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression is developed and the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
Journal ArticleDOI
Unsupervised feature selection using feature similarity
TL;DR: An unsupervised feature selection algorithm suitable for data sets, large in both dimension and size, based on measuring similarity between features whereby redundancy therein is removed, which does not need any search and is fast.