BS-GAENets: Brain-Spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis

1. Demirci, O., Clark, V.P., Magnotta, V.A., et al.: A review of challenges in the use of fMRI for disease classification/characterization and a projection pursuit application from a multi-site fMRI schizophrenia study. Brain Imaging Behav. 2(3), 207–226 (2008)

   Article  Google Scholar 

2. Mihalik, A., Ferreira, F.S., Rosa, M.J., et al.: Brain-behaviour modes of covariation in healthy and clinically depressed young people. Sci. Rep. 9, 1–11 (2019)

   Article  Google Scholar 

3. Sui, J., Adali, T., Yu, Q., et al.: A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods 204(1), 68–81 (2012)

   Article  Google Scholar 

4. Sui, J., Pearlson, G.D., et al.: In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia. Biol. Psychiat. 78(11), 794–804 (2015)

   Article  Google Scholar 

5. Blackmon, K., Barr, W.B., Kuzniecky, R., et al.: Phonetically irregular word pronunciation and cortical thickness in the adult brain. Neuroimage 51(4), 1453–1458 (2010)

   Article  Google Scholar 

6. Saygin, Z.M., Osher, D.E., et al.: Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat. Neurosci. 15(2), 321–327 (2012)

   Article  Google Scholar 

7. Du, W., Calhoun, V.D., et al.: High classification accuracy for schizophrenia with rest and task FMRI data. Front. Hum. Neurosci. 6(145) (2012)

   Google Scholar 

8. Hanachi, R., Sellami, A., Farah, I.: Interpretation of human behavior from multi-modal brain mri images based on graph deep neural networks and attention mechanism. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4: VISAPP, pp. 56–66 (2021)

   Google Scholar 

9. Jiang, J., Ma, J., Chen, C., et al.: SuperPCA: A superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 56(8), 4581–4593 (2018)

   Article  Google Scholar 

10. He, X., Cai, D., Yan, S., Zhang, H.-J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), pp. 1208–1213 (2005)

    Google Scholar 

11. Ma, L., Crawford, M.M., Tian, J.: Anomaly detection for hyperspectral images based on robust locally linear embedding. J. Infrared Milli Terahz Waves 31, 753–762 (2010)

    Google Scholar 

12. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)

    Article  MATH  Google Scholar 

13. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk. In: Proceedings of the 20th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining - KDD 2014 (2014)

    Google Scholar 

14. Dong, Y., Chawla, N.V., Swami, A.: Metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), pp. 135–144. Association for Computing Machinery, New York (2017)

    Google Scholar 

15. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp, 855–864. Association for Computing Machinery (2016)

    Google Scholar 

16. Wu, Z., Pan, S., Chen, F., Long, G., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 1–21 (2020)

    Article  Google Scholar 

17. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), pp. 1025–1035. Curran Associates Inc., Red Hook (2017)

    Google Scholar 

18. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML, pp. 689–696 (2011)

    Google Scholar 

19. Alam, M.T., Kumar, V., Kumar, A.: A Multi-view convolutional neural network approach for image data classification. In: 2021 International Conference on Communication information and Computing Technology (ICCICT), pp. 1–6 (2021)

    Google Scholar 

20. Sun, C., Yuan, Y.-H., Li, Y., Qiang, J., Zhu, Y., Shen, X.: Multi-view fractional deep canonical correlation analysis for subspace clustering. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13109, pp. 206–215. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92270-2_18

    Chapter  Google Scholar 

21. Bhatt, G., Jha, P., Raman, B.: Representation learning using step-based deep multi-modal autoencoders. Pattern Recogn. 95, 12–23 (2019)

    Article  Google Scholar 

22. Khosla, M., Setty, V., Anand, A.: A comparative study for unsupervised network representation learning. IEEE Trans. Knowl. Data Eng. (2020)

    Google Scholar 

23. Mikolov, T., Chen, K.: Greg Corrado. Efficient Estimation of Word Representations in Vector Space, Jeffrey Dean (2013)

    Google Scholar 

24. Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: A survey. CoRR (2018)

    Google Scholar 

25. Kadri, H., Ayache, S., Huusari, R., Rakotomamonjy, A., Ralaivola, L.: Partial trace regression and low-rank kraus decomposition. In: International Conference on Machine Learning (2020)

    Google Scholar 

26. Koltchinskii, V., Lounici, K., Tsybakov, A.B.: Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion. Ann. Stat. 39(5), 2302–2329 (2011)

    Article  MATH  Google Scholar 

27. Fan, J., Gong, W., Zhu, Z.: Generalized high dimensional trace regression via nuclear norm regularization. J. Econom. 212(1), 177–202 (2019)

    Article  MATH  Google Scholar 

Download references