Advances in Neural Networks – ISNN 2016: 13th International by Long Cheng, Qingshan Liu, Andrey Ronzhin

By Long Cheng, Qingshan Liu, Andrey Ronzhin

This booklet constitutes the refereed complaints of the thirteenth overseas Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers awarded during this quantity have been rigorously reviewed and chosen from 104 submissions. The papers hide many issues of neural network-related examine together with sign and snapshot processing; dynamical behaviors of recurrent neural networks; clever keep watch over; clustering, category, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.

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Independent component analysis: recent advances. Proc. R. Soc. A Math. Phys. Eng. Sci. 371, 1–19 (2013) 9. : Functionally independent components of the late positive event-related potential during visual spatial attention. J. Neurosci. 19(7), 2665–2680 (1999) 10. : Analysis on subtracting projection of extracted independent components from EEG recordings. Biomedizinische Technik/Biomed. Eng. 56(4), 223–234 (2011) 11. : Validating the independent components of neuroimaging time series via clustering and visualization.

31(1), 55–66 (2014) 9. : A novel supervised dimensionality reduction algorithm: graph-based fisher analysis. Pattern Recogn. 45(4), 1471–1481 (2012) 10. : Locality preserving projections. In: Neural Information Processing Systems, vol. 16, p. 153 (2004) 11. : Neighborhood preserving embedding. In: IEEE International Conference on Computer Vision, pp. 1208–1213 (2005) 12. : Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027–1061 (2007) 13.

To extract the spatial information that contain spatially homogeneous property and distinct boundary, the original hyperspectral image is processed by an improved bilateral filter firstly. And then the proposed feature extraction algorithm called locality preserving discriminant analysis, which can explore the manifold structure and intrinsic characteristics of the hyperspectral dataset, is used to reduce the dimensionality of both the spectral and spatial features. Finally, a support vector machine with a composite kernel is used to examine the performance of the proposed methods.

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