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JIA X, KUO B C, and CRAWFORD M M. Feature mining for Hyper-spectral image classification[J]. Proceedings of the IEEE, 2013, 101(3):676-697.10.1109/JPROC.2012.2229082Search in Google Scholar
Wang Liping. FEATURE SELECTION ALGORITHM BASED ON CONDITIONAL DYNAMIC MUTUAL INFORMATION [J].INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, 2015, 8(1):316-33710.21307/ijssis-2017-761Search in Google Scholar
HUO LEI-GANG, FENG XIANG-CHU. Denoising of Hyperspectral Remote Sensing Image Based on Principal Component Analysis and Dictionary Learning[J]. Journal of Electronics & Information Technology,2014,36(11):2723-2729.Search in Google Scholar
CHENG SHU-XI, XIE CHUAN-QI, WANG QIAO-NAN, et al. Different Wavelengths Selection Methods for Identification of Early Blight on Tomato Leaves By Using Hyperspectral Imaging Technique[J]. Spectroscopy and Spectral Analysis,2014,34(5):1362-1366.Search in Google Scholar
FAN LIHENG, LV JUNWEI, and DENG JIANGSHENG. Classification of Hyperspectral Remote Sensing Images Based on Bands Grouping and Classification Ensembles[J]. ACTA OPTICA SINICA,2014,VOl.34,No.9:1-11.10.3788/AOS201434.0910002Search in Google Scholar
CHERIYADAT A, BRUCE L. Why principal component analysis is not an appropriate feature extraction method for hyperspectral data[J].Proceeding of IEEE Geoscience and Remote Sensing Symposium(IGARSS),2003,104(2):3420-3422.10.1109/IGARSS.2003.1294808Search in Google Scholar
SUN KANG, GENG XIURUI, TANG HAIRONG, et al. A New Target Detection Method Using Nonlinear PCA for Hyperspectral Imagery [J].Bulletin of Surveying and Mapping, 2015(1):105-108.Search in Google Scholar
LIU JING. Kernel Direct LDA Subspace Hyperspectral Image Terrain Classification[J].Computer Science,2012,39(6): 274-277.Search in Google Scholar
DC FENG, F CHEN, and XU WEN-LI. Detecting Local Manifold Structure for Unsupervised Feature Selection [J]. Acta Automatica Sinica, 2014, 40(10):2253-2261.10.1016/S1874-1029(14)60362-1Search in Google Scholar
S ZHOU, K TAN, and L WU. Hyperspectral Image Classification based on ISOMAP Algorithm using Neighborhood Distance[J].Remote Sensing Technology & Application, 2014, 29(4):695-700.Search in Google Scholar
L YAN, DP ROY. Improved time series land cover classification by missing-observation- adaptive nonlinear dimensionality reduction[J].Remote Sensing of Environment, 2015, 158:478491.10.1016/j.rse.2014.11.024Search in Google Scholar
W SUN, C LIU, B SHI, et al. Dimensionality Reduction with Improved Local Tangent Space Alignment for Hyperspectral Imagery Classification [J].Journal of Tongji University, 2014, 42(1):0124-0130.Search in Google Scholar
HINTON G, OSINDERO S, and TEH Y W. A fast learning algorithm for deep belief nets [J].Neural computation, 2006, 18(7):1527-1554.10.1162/neco.2006.18.7.152716764513Search in Google Scholar
HINTON, GEOFFREY E, and RUSLAN R. Reducing the Dimensionality of Data with Neural Networks [J].Science, 2006, 313(5786):504-507.10.1126/science.112764716873662Search in Google Scholar
YANGYAN LI, HAO SU, CHARLES RUIZHONGTAI QI, et al. Joint embeddings of shapes and images via CNN image purification [J]. Acm Transactions on Graphics, 2015, 34(6):1-12.10.1145/2816795.2818071Search in Google Scholar
HINTON G, DENG L, YU D, et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition [J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.10.1109/MSP.2012.2205597Search in Google Scholar
DAHL G E, YU D, DENG L, et al. Context-Dependent Pre-trained Deep Neural Networks for Large-Vocabulary Speech Recognition [J]. IEEE Trans on Audio, Speech and Language Processing, 2012,20(1):30-42.10.1109/TASL.2011.2134090Search in Google Scholar
J XIE, L ZHANG, J YOU, et al. Effective texture classification by texton encoding induced statistical features [J]. Pattern Recognition, 2015, 48(2):447–457.10.1016/j.patcog.2014.08.014Search in Google Scholar
MNIH V, HINTON G E. Learning to Detect Roads in High-Resolution Aerial Images [J]. Lecture Notes in Computer Science, 2010, 6316:210-223.10.1007/978-3-642-15567-3_16Search in Google Scholar
HINTON G E, SEJNOWSKI T J. Optimal perceptual inference[C].Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE Press, 1983: 448-453.Search in Google Scholar
RD HJELM, VD CALHOUN, R SALAKHUTDINOV, et al. Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks[J]. Neuroimage, 2014, 96(8):245-260.10.1016/j.neuroimage.2014.03.048434802124680869Search in Google Scholar
LE ROUX, NICOLAS, YOSHUA BENGIO. Representational power of restricted boltzmann machines and deep belief networks[J]. Neural Computation, 2008, 20(6): 1631-1649.10.1162/neco.2008.04-07-51018254699Search in Google Scholar
J BA, R GROSSE, R SALAKHUTDINOV, et al. Learning Wake-Sleep Recurrent Attention Models[J]. Conference on Neural Information Processing Systems, Canada, 2015:1-9.Search in Google Scholar
YOSHUA BENGIO, PASCAL LAMBLIN, DAN POPOVICI, et al. Greedy layer-wise training of deep networks[C]. Advances in Neural Information Processing Systems 19 (NIPS 2006).Vancouver, 2007: 153-160.Search in Google Scholar
HINTON G E. A Practical Guide to Training Restricted Boltzmann Machines[R], Montreal: Department of Computer Science, University of Toronto, 2010Search in Google Scholar
HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8):1771-1800.10.1162/08997660276012801812180402Search in Google Scholar
LE ROUX, NICOLAS, YOSHUA BENGIO. Representational power of restricted boltzmann machines and deep belief networks [J]. Neural Computation, 2008, 20(6): 1631-1649.10.1162/neco.2008.04-07-510Search in Google Scholar
ZHANG CHUN-XIA, JI NAN-NAN, and WANG GUAN-WEI. Restricted Boltzmann Machines[J]. Chinese Journal of Engineering Mathematics, 2015, 32(2):159-173.Search in Google Scholar
RN LE, Y BENGIO. Representational power of restricted boltzmann machines and deep belief networks [J]. Neural Computation, 2008, 20(6):1631-1649.10.1162/neco.2008.04-07-510Search in Google Scholar
H LAROCHELLE, Y BENGIO, LOURADOUR, et al. Exploring Strategies for Training Deep Neural Networks [J]. Journal of Machine Learning Research, 2009, 10(6):1-40.Search in Google Scholar
Yongqing Wang,Yanzhou Zhang. NOVEL MULTI-CLASS SVM ALGORITHM FOR MULTIPLE OBJECT RECOGNITION[J].INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS,2015,8(2):1203-122410.21307/ijssis-2017-803Search in Google Scholar
Yongqing Wang, Xiling Liu. FACE RECOGNITION BASED ON IMPROVED SUPPORT VECTOR CLUSTERING[J].INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS,2014,7(4):1807-182910.21307/ijssis-2017-734Search in Google Scholar