Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Key observation of geometric analysis of two different time series construction criterions. There are two subspace S1 and S2. Points A, B, C, and D are drawn from S1, points E and F from S2. (a) k-NN criterion to construct time series: points E and A are likely to construct a time series in terms of Euclidean distance but they are not in the same submanifold. (b) Our method: points A, B, and D are likely to construct a time series as they lie in the same principal direction. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Sample images in AR face database. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Recognition accuracy of principal component analysis (PCA), linear discriminant analysis (LDA), neighborhood preserving embedding (NPE), unsupervised discriminant projection (UDP), unsupervised slow feature analysis (USFA), supervised slow feature analysis (SSFA), and adaptive unsupervised slow feature analysis (AUSFA) in AR database using seven images as training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Recognition rates of AUSFA and KUSFA with a different parameter k in AR database using five training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Sample images in PIE database. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Recognition accuracy of PCA, LDA, NPE, UDP, USFA, SSFA, and AUSFA in PIE database using seven images as training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Recognition rates of AUSFA and KUSFA with a different parameter k in PIE database using five training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Sample images in YaleB database. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. Recognition accuracy of PCA, LDA, NPE, UDP, USFA, SSFA, and AUSFA in YaleB database using seven images as training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI

Date of download: 9/18/2016 Copyright © 2016 SPIE. All rights reserved. The maximum recognition accuracy with the variants of parameter α on the AR, PIE, and YaleB face databases using five training samples. Figure Legend: From: Adaptive unsupervised slow feature analysis for feature extraction J. Electron. Imaging. 2015;24(2): doi: /1.JEI