Fast semi-supervised learning with anchor graph for large hyperspectral images

Abstract As the labeled samples of hyperspectral image (HSI) are very scarce and labeling sample costs too much time and is expensive, semi-supervised learning (SSL) has an important application in hyperspectral image (HSI) classification. Among SSL approaches, graph-based SSL (GSSL) model has recently attracted much attention. However, most GSSL methods still can not deal with the large HSI as their high computational complexity. In this letter, we propose a novel approach, called fast semi-supervised learning with anchor graph (FSSLAG) to solve the large HSI classification problem. In the proposed FSSLAG algorithm, the anchor graph, which is parameter-free, naturally sparse and scale invariant, is first constructed. Then the label of samples can be inferred through the graph. The computational complexity of FSSLAG can be reduced to O(ndm), which is a significant improvement compared with traditional graph-based SSL methods that need O(n3), where n, d and m are the number of samples, features and anchors, respectively. Several experiments have demonstrated the effectiveness and efficiency of FSSLAG in terms of computational speed and classification accuracy.

[1]  Shih-Fu Chang,et al.  Learning with Partially Absorbing Random Walks , 2012, NIPS.

[2]  Yi Yang,et al.  Semantic Pooling for Complex Event Analysis in Untrimmed Videos , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Rong Wang,et al.  Stable and orthogonal local discriminant embedding using trace ratio criterion for dimensionality reduction , 2018, Multimedia Tools and Applications.

[4]  Zhihui Li,et al.  Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis , 2017, IEEE Transactions on Knowledge and Data Engineering.

[5]  Li Ma,et al.  Probabilistic class structure regularized sparse representation graph for semi-supervised hyperspectral image classification , 2017, Pattern Recognit..

[6]  Meng Wang,et al.  Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization , 2016, IEEE Transactions on Knowledge and Data Engineering.

[7]  Xiaojun Chang,et al.  Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Lorenzo Bruzzone,et al.  Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Zenglin Xu,et al.  Unified Spectral Clustering with Optimal Graph , 2017, AAAI.

[10]  Jon Atli Benediktsson,et al.  Semisupervised Self-Learning for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Rui Zhang,et al.  Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine , 2014, IEEE Geoscience and Remote Sensing Letters.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  Zhao Kang,et al.  Kernel-driven similarity learning , 2017, Neurocomputing.

[14]  Chengqi Zhang,et al.  Convex Sparse PCA for Unsupervised Feature Learning , 2014, ACM Trans. Knowl. Discov. Data.

[15]  Feiping Nie,et al.  The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.

[16]  Jun Li,et al.  Sparse Graph Regularization for Hyperspectral Remote Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Zhi-Hua Zhou,et al.  Semisupervised Regression with Cotraining-Style Algorithms , 2007, IEEE Transactions on Knowledge and Data Engineering.

[18]  Gustavo Camps-Valls,et al.  Semisupervised Classification of Remote Sensing Images With Active Queries , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Marco Loog,et al.  Robust semi-supervised least squares classification by implicit constraints , 2015, Pattern Recognit..

[20]  Rong Wang,et al.  Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[21]  Xuelong Li,et al.  Unsupervised Large Graph Embedding , 2017, AAAI.

[22]  Feiping Nie,et al.  A general graph-based semi-supervised learning with novel class discovery , 2010, Neural Computing and Applications.

[23]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Qinghua Zheng,et al.  Adaptive Unsupervised Feature Selection With Structure Regularization , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[26]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[27]  Jun Li,et al.  Improved hyperspectral image classification by active learning using pre-designed mixed pixels , 2016, Pattern Recognit..

[28]  Wei Liu,et al.  Large Graph Construction for Scalable Semi-Supervised Learning , 2010, ICML.

[29]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression , 2013, IEEE Geoscience and Remote Sensing Letters.

[30]  Yi Yang,et al.  A Convex Formulation for Semi-Supervised Multi-Label Feature Selection , 2014, AAAI.

[31]  Qinghua Zheng,et al.  An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition , 2018, IEEE Transactions on Cybernetics.