Sparse principal component analysis for feature selection of multiple physiological signals from flight task

2015 15th International Conference on Control, Automation and Systems (ICCAS) ◽

10.1109/iccas.2015.7364994 ◽

2015 ◽

Author(s):

Dongbo Bai ◽

Wei Liming ◽

Wei Chan ◽

Qi Wu ◽

Dan Huang ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

Physiological Signals ◽

Sparse Principal Component Analysis ◽

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Modified Principal Component Analysis (MPCA) for feature selection of hyperspectral imagery

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) ◽

10.1109/igarss.2003.1295268 ◽

2004 ◽

Author(s):

Cheng Wang ◽

M. Menenti ◽

Zhao-Liang Li

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Hyperspectral Imagery ◽

Principal Component ◽

Component Analysis ◽

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Selection of non-zero loadings in sparse principal component analysis

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2017.01.018 ◽

2017 ◽

Vol 162 ◽

pp. 160-171 ◽

Author(s):

Shriram Gajjar ◽

Murat Kulahci ◽

Ahmet Palazoglu

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis ◽

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Clustering and feature selection using sparse principal component analysis

Optimization and Engineering ◽

10.1007/s11081-008-9057-z ◽

2008 ◽

Vol 11 (1) ◽

pp. 145-157 ◽

Author(s):

Ronny Luss ◽

Alexandre d’Aspremont

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis

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Feature selection of weather data with interval principal component analysis

2016 International Conference on System Science and Engineering (ICSSE) ◽

10.1109/icsse.2016.7551600 ◽

2016 ◽

Author(s):

Chong-Cheng He ◽

Jin-Tsong Jeng

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

Weather Data ◽

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Feature Selection of Oral Cyst and Tumor Images Using Principal Component Analysis

2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT) ◽

10.1109/eiconcit.2018.8878641 ◽

2018 ◽

Author(s):

Syahrul Mubarak ◽

Herdianti Darwis ◽

Fitriyani Umar ◽

Lutfi Budi Ilmawan ◽

Siska Anraeni ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

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Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2019.2928755 ◽

2020 ◽

Vol 31 (6) ◽

pp. 2153-2163 ◽

Author(s):

Shuangyan Yi ◽

Zhenyu He ◽

Xiao-Yuan Jing ◽

Yi Li ◽

Yiu-Ming Cheung ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis ◽

Unsupervised Feature Selection

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Feature selection of stabilometric parameters based on principal component analysis

Medical & Biological Engineering & Computing ◽

10.1007/bf02351013 ◽

2004 ◽

Vol 42 (1) ◽

pp. 71-79 ◽

Author(s):

L. Rocchi ◽

L. Chiari ◽

A. Cappello

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

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Feature selection of postural summary statistic scores based on principal component analysis in Parkinson’s disease

Modelling in Medicine and Biology VI ◽

10.2495/bio050511 ◽

2005 ◽

Author(s):

L. Rocchi ◽

L. Chiari ◽

A. Cappello ◽

F. B. Horak

Keyword(s):

Parkinson’S Disease ◽

Principal Component Analysis ◽

Parkinson's Disease ◽

Feature Selection ◽

Principal Component ◽

Component Analysis ◽

Summary Statistic ◽

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Feature selection of manifold learning using principal component analysis in brain MR image

2015 International Conference on Informatics, Electronics & Vision (ICIEV) ◽

10.1109/iciev.2015.7334065 ◽

2015 ◽

Author(s):

Saadia Binte Alam ◽

Ryosuke Nakano ◽

Syoji Kobashi ◽

Naotake Kamiura

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Manifold Learning ◽

Principal Component ◽

Component Analysis ◽

Mr Image ◽

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Selection Of Variables And Indicators In Financial Distress Prediction Model-Svm Method Based On Sparse Principal Component Analysis

2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) ◽

10.1109/icwapr51924.2020.9494625 ◽

2020 ◽

Author(s):

Sen Zeng ◽

Wanjun Yang

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Financial Distress ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis ◽

Financial Distress Prediction ◽

Selection Of Variables ◽

Distress Prediction ◽

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