nach oben

World Wide Web

Erschienen in:

13.11.2018

Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification

verfasst von: Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen

Erschienen in: World Wide Web | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we propose a novel dimensionality reduction method of taking the advantages of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer’s Disease (AD) classification. We first take the variability of neuroimaging data into account by partitioning them into sub-classes by means of clustering, which thus captures the underlying multi-peak distributional characteristics in neuroimaging data. We then iteratively conduct Low-Rank Dimensionality Reduction (LRDR) and orthogonal rotation in a sparse linear regression framework, in order to find the low-dimensional structure of high-dimensional data. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that our proposed model helped enhance the performances of AD classification, outperforming the state-of-the-art methods.

Vorheriger Artikel Practical k-agents search algorithm towards information retrieval in complex networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

http://www.loni.usc.edu/ADNI.

Besides, the remaining 84 MCI subjects include 33 subjects that did not convert in 24 months but converted in 36 months, and 51 subjects that were MCI at base line but were missed at any available time points among 0 – 96 months.

http://mipav.cit.nih.gov/clickwrap.php.

In this work, we extract the same number of features from MRI and PET as described in Section 2.1 and thus their feature dimensions are the same. However, it should be noted that the proposed method can be easily extended to multiple modalities with different numbers of features. Moreover, in the multi-modality case of this work, r < min{rank(B_i),rank(A_i)} or r < min{rank(B_i),rank(A)}, i = 1, 2.

In our experiments, we used matlab function ‘floor’ to discritize the real values of r.

In Tables 2–5, the boldface denotes the maximum performance in each column. (Symbols * and ◇, respectively, represent statistically significant difference between the proposed method and the comparison methods under p < 0.05 and p < 0.001, on the paired-sample t-tests at 95% significance level.)

Chételat, G., Eustache, F., Viader, F., De La Sayette, V., Pélerin, A., Mézenge, F., Hannequin, D., Dupuy, B., Baron, J.-C., Desgranges, B.: FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11(1), 14–25 (2005)CrossRef

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., Lin, C.: Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1), 59–70 (2012)CrossRef

Deng, X., Li, Y., Weng, J., Zhang, J.: Feature selection for text classification: A review. Multimedia Tools and Applications, 1–20 (2018)

Fox, N.C., Schott, J.M.: Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet 363(9406), 392–394 (2004)CrossRef

Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimed. 19(9), 2045–2055 (2017)CrossRef

Gower, J.C., Dijksterhuis, G.B.: Procrustes problems, vol. 3 (2004)

Hu, R., Zhu, X., Cheng, D., He, W., Yan, Y., Song, J., Zhang, S.: Graph self-representation method for unsupervised feature selection. Neurocomputing 220, 130–137 (2017)CrossRef

Izenman, A.J.: Reduced-rank regression for the multivariate linear model. J. Multivar. Anal. 5(2), 248–264 (1975)MathSciNetCrossRefMATH

Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)CrossRefMATH

10.

Jorgensen, M.: Iteratively reweighted least squares. Encyclopedia of Environmetrics (2006)

11.

Kabani, N.J.: 3D anatomical atlas of the human brain. In: Human Brain Mapping Conference (1998)

12.

Lahmiri, S.: Image characterization by fractal descriptors in variational mode decomposition domain: application to brain magnetic resonance. Physica A: Stat. Mech. Appl. 456, 235–243 (2016)MathSciNetCrossRefMATH

13.

Lahmiri, S., Boukadoum, M.: New approach for automatic classification of Alzheimer’s disease, mild cognitive impairment and healthy brain magnetic resonance images. Healthcare Technol. Lett. 1(1), 32–36 (2014)CrossRef

14.

Lei, C., Zhu, X.: Unsupervised feature selection via local structure learning and sparse learning. https://doi.org/10.1007/s11042--017--5381--7, p. 11 (2017)

15.

Liu, F., Wee, C.-Y., Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for alzheimer’s disease and mild cognitive impairment identification. Neuroimage 84, 466–475 (2014)CrossRef

16.

Lu, C., Lin, Z., Yan, S.: Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization. IEEE Trans. Image Process. 24(2), 646–654 (2015)MathSciNetCrossRefMATH

17.

Ma, Z., Sun, T.: Adaptive sparse reduced-rank regression. arXiv:1403.1922 (2014)

18.

Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44(4), 1415–1422 (2009)CrossRef

19.

Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Alzheimer’s Disease Neuroimaging Initiative, et al.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015)CrossRef

20.

Noppeney, U., Penny, W.D., Price, C.J., Flandin, G., Friston, K.J.: Identification of degenerate neuronal systems based on intersubject variability. Neuroimage 30(3), 885–890 (2006)CrossRef

21.

Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRef

22.

Rocchi, L., Chiari, L., Cappello, A., Horak, F.B: Identification of distinct characteristics of postural sway in parkinson’s disease: a feature selection procedure based on principal component analysis. Neurosci. Lett. 394(2), 140–145 (2006)CrossRef

23.

Sato, J.R., Hoexter, M.Q., Fujita, A., Rohde, L.A.: Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Frontiers in Systems Neuroscience, 6 (2012)

24.

Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration, vol. 21 (2002)

25.

Song, J., Guo, Y., Gao, L., Li, X., Hanjalic, A., Shen, H.T.: From deterministic to generative: Multi-modal stochastic rnns for video captioning. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2018.2851077 (2017)

26.

Song, J., Zhang, H., Li, X., Gao, L., Wang, M., Hong, R.: Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans. Image Process. 27 (7), 3210–3221 (2018)MathSciNetCrossRefMATH

27.

Sui, J., Adali, T., Yu, Q., Chen, J., Calhoun, V.D.: A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods 204(1), 68–81 (2012)CrossRef

28.

Velu, R., Reinsel, G.C.: Multivariate reduced-rank regression: theory and applications, vol. 136 (2013)

29.

Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A.J., Li, S.: Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In: MICCAI, pp. 115–123 (2011)

30.

Wang, X., Gao, L., Wang, P., Sun, X., Liu, X.: Two-stream 3-d convnet fusion for action recognition in videos with arbitrary size and length. IEEE Trans. Multimed. 20(3), 634–644 (2018)CrossRef

31.

Yang, M., Zheng, H., Wang, H., McClean, S.: Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. In: PCTH, pp. 1–7 (2009)

32.

Yang, Y., Zhou, J., Ai, J., Yi, B., Hanjalic, A., Shen, H.T.: Video captioning by adversarial lstm. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2018.2855422 (2018)

33.

Yi, B., Yang, Y., Shen, F., Xie, N., Shen, H.T., Li, X.: Describing video with attention based bidirectional lstm. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2018.2831447 (2018)

34.

Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59 (2), 895–907 (2012)MathSciNetCrossRef

35.

Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRef

36.

Zhang, S., Li, X., Zong, M., Zhu, X., Wang, R.: Efficient knn classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 29 (5), 1774–1785 (2018)MathSciNetCrossRef

37.

Zheng, W., Zhu, X., Zhu, Y., Hu, R., Lei, C.: Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications. https://doi.org/10.1007/s11042--017--5272--y (2017)

38.

Zheng, W., Zhu, X., Wen, G., Zhu, Y., Yu, H., Gan, J.: Unsupervised feature selection by self-paced learning regularization. Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2018.06.029 (2018)

39.

Zhu, M., Martinez, A.M: Subclass discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1274–1286 (2006)CrossRef

40.

Zhu, X., Suk, H.-I., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 14(0), 1–30 (2014)CrossRef

41.

Zhu, X., Zhang, L., Zi, H.: A sparse embedding and least variance encoding approach to hashing. IEEE Trans. Image Process. 23(9), 3737–3750 (2014)MathSciNetCrossRefMATH

42.

Zhu, X., Li, X., Zhang, S.: Block-row sparse multiview multilabel learning for image classification. IEEE Trans. Cybern. 46(2), 450–461 (2016)CrossRef

43.

Zhu, X., Suk, H.-I., Lee, S.-W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)CrossRef

44.

Zhu, X., Li, X., Zhang, S., Ju, C., Wu, X.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans. Neural Netw. Learn. Syst. 28 (6), 1263–1275 (2017)MathSciNetCrossRef

45.

Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., Wang, C.: Graph pca hashing for similarity search. IEEE Trans. Multimed. 19(9), 2033–2044 (2017)CrossRef

46.

Zhu, X., Suk, H.-I., Huang, H., Shen, D.: Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Trans. Big Data 3(4), 405–414 (2017)CrossRef

47.

Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., Shen, D.: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38, 205–214 (2017)CrossRef

48.

Zhu, X., Zhang, S., He, W., Hu, R., Lei, C., Zhu, P.: One-step multi-view spectral clustering. IEEE Transactions on Knowledge and Data Engineering, https://doi.org/10.1109/TKDE.2018.2873378 (2018)

49.

Zhu, X., Zhang, S., Hu, R., Zhu, Y., et al.: Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans. Knowl. Data Eng. 30(3), 517–529 (2018)CrossRef

50.

Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., Fang, Y.: Low-rank sparse subspace for spectral clustering. IEEE Transactions on Knowledge and Data Engineering, https://doi.org/10.1109/TKDE.2018.2858782 (2018)

Titel: Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification
verfasst von: Xiaofeng Zhu
Heung-Il Suk
Dinggang Shen
Publikationsdatum: 13.11.2018
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 2/2019
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-018-0645-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2019

Co-regularized kernel ensemble regression

Class consistent hashing for fast Web data searching

Deep learning approaches for video-based anomalous activity detection

Predicting e-book ranking based on the implicit user feedback

On fusing the latent deep CNN feature for image classification

Deep adversarial metric learning for cross-modal retrieval