Hierarchical Manifold Learning With Applications to Supervised Classification for High-Resolution Remotely Sensed Images
暂无分享,去创建一个
Hong Huo | Tao Fang | Hong-Bing Huang | Hong Huang | T. Fang | H. Huo | T. Fang
[1] Peter Tiño,et al. Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model , 2008, ECML/PKDD.
[2] Donald F. Specht,et al. The general regression neural network - Rediscovered , 1993, Neural Networks.
[3] Stephen Lin,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Joydeep Ghosh,et al. Improved Nonlinear Manifold Learning for Land Cover Classification via Intelligent Landmark Selection , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.
[5] Antonio Torralba,et al. Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Hongbin Zha,et al. Riemannian Manifold Learning , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Kilian Q. Weinberger,et al. Unsupervised Learning of Image Manifolds by Semidefinite Programming , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[8] Zhi-Hua Zhou,et al. Supervised nonlinear dimensionality reduction for visualization and classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[9] H. Zha,et al. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..
[10] I. T. Jolliffe,et al. Generalizations and Adaptations of Principal Component Analysis , 1986 .
[11] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[12] David J. Kriegman,et al. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.
[13] Lawrence K. Saul,et al. Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..
[14] Honggang Zhang,et al. Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" , 2007, IEEE Trans. Pattern Anal. Mach. Intell..
[15] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[16] Thomas L. Ainsworth,et al. Bathymetric Retrieval From Hyperspectral Imagery Using Manifold Coordinate Representations , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[17] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.
[18] Mikhail Belkin,et al. Using manifold structure for partially labelled classification , 2002, NIPS 2002.
[19] Anil K. Jain,et al. Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[20] Qijun Zhao,et al. Facial expression recognition on multiple manifolds , 2011, Pattern Recognit..
[21] Hong Huo,et al. Supervised classification of multispectral remote sensing images based on the nearest reduced convex hull approach , 2009 .
[22] Thomas L. Ainsworth,et al. Exploiting manifold geometry in hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[23] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[24] Thomas L. Ainsworth,et al. Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[25] Yuxiao Hu,et al. Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] H. Deutsch. Principle Component Analysis , 2004 .
[27] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[28] Yen-Wei Chen,et al. Classification of High-Resolution Satellite Images Using Supervised Locality Preserving Projections , 2008, KES.
[29] D. Flanders,et al. Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction , 2003 .
[30] Christopher M. Bishop,et al. A Hierarchical Latent Variable Model for Data Visualization , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[31] David J. Kriegman,et al. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.
[32] Joydeep Ghosh,et al. Multiresolution manifold learning for classification of hyperspectral data , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.
[33] Stan Z. Li,et al. Manifold Learning and Applications in Recognition , 2005 .
[34] Kilian Q. Weinberger,et al. Unsupervised Learning of Image Manifolds by Semidefinite Programming , 2004, CVPR.
[35] Kilian Q. Weinberger,et al. An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding , 2006, AAAI.
[36] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[37] Xi Chen,et al. Graph-Based Feature Selection for Object-Oriented Classification in VHR Airborne Imagery , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[38] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[39] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[40] Hwann-Tzong Chen,et al. Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[41] H. Sebastian Seung,et al. The Manifold Ways of Perception , 2000, Science.
[42] Carlo Vercellis,et al. Kernel ridge regression for out-of-sample mapping in supervised manifold learning , 2012, Expert Syst. Appl..
[43] Gunnar Rätsch,et al. Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[44] Mineichi Kudo,et al. Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..
[45] María José del Jesús,et al. Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems , 2001, Inf. Sci..
[46] Matti Pietikäinen,et al. Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine , 2003, ESANN.
[47] Adam Krzyzak,et al. Piecewise Linear Skeletonization Using Principal Curves , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[48] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[49] Wenbin Chen,et al. Manifold Learning for Image Denoising , 2005, The Fifth International Conference on Computer and Information Technology (CIT'05).
[50] Xiangyang Xue,et al. Metric learning by discriminant neighborhood embedding , 2008, Pattern Recognit..
[51] Chi Hau Chen,et al. Statistical pattern recognition in remote sensing , 2008, Pattern Recognit..
[52] Jian Yang,et al. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[54] Ben Kröse,et al. The generative self-organizing map: a probabilistic generalization of Kohonen's SOM , 2002 .
[55] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[56] Visar Berisha,et al. Sparse Manifold Learning with Applications to SAR Image Classification , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.