Approximative Bayes optimality linear discriminant analysis for Chinese handwriting character recognition

Discriminant subspace learning is an important branch for pattern recognition and machine learning. Among the various methods, Bayes optimality linear discriminant analysis (BLDA) has shown its superiority both in theory and application. However, due to the computational complexity, BLDA has not been applied to large category pattern tasks yet. In this paper, we propose an approximative Bayes optimality linear discriminant analysis (aBLDA) method for Chinese handwriting character recognition, which is a typical large category task. In the aBLDA, we first select a set of convex polyhedrons that are obtained by the state-of-the-art methods, then the searching zones are limited to these polyhedrons. Finally, the best of them is chosen as the final projection. In this way, the computational complexity of BLDA is reduced greatly with comparable accuracy. To find more than 1D projections, the orthogonal constraint is employed in the proposed method. The experimental results on synthetic data and CASIA-HWDB1.1 show the effectiveness of the proposed method.

[1]  Ravi Kothari,et al.  Fractional-Step Dimensionality Reduction , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  R. Sakia.  The Box-Cox transformation technique: a review , 1992 .

[3]  Lianwen Jin,et al.  Building compact MQDF classifier for large character set recognition by subspace distribution sharing , 2008, Pattern Recognit..

[4]  Junwei Han,et al.  Multi-class geospatial object detection and geographic image classification based on collection of part detectors , 2014 .

[5]  Changsong Liu,et al.  Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[6]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[7]  Aleix M. Martínez,et al.  Bayes Optimality in Linear Discriminant Analysis , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ka-Chung Leung,et al.  Recognition of handwritten Chinese characters by critical region analysis , 2010, Pattern Recognit..

[9]  Lei Guo,et al.  Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images , 2015, IET Comput. Vis..

[10]  Junwei Han,et al.  Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding , 2014 .

[11]  Cheng-Lin Liu,et al.  High accuracy handwritten Chinese character recognition using LDA-based compound distances , 2008, Pattern Recognit..

[12]  Xiang Ji,et al.  Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space , 2013, IEEE Transactions on Image Processing.

[13]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[14]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[15]  C. McDermott.  Discrimination , 2009, Inclusive Equality.

[16]  M. Schervish.  Linear discrimination for three known normal populations , 1984 .

[17]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[18]  Fumitaka Kimura,et al.  Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Cheng-Lin Liu,et al.  Confused Distance Maximization for Large Category Dimensionality Reduction , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[20]  Zhaoyang Lu,et al.  An improved Fisher discriminant vector employing updated between-scatter matrix , 2016, Neurocomputing.

[21]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[22]  Fei Yin,et al.  CASIA Online and Offline Chinese Handwriting Databases , 2011, 2011 International Conference on Document Analysis and Recognition.

[23]  Seymour Geisser,et al.  Discrimination, Allocatory and Separatory, Linear Aspects , 1977 .

[24]  Lei Guo,et al.  An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jieping Ye,et al.  Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis , 2006, IEEE Transactions on Knowledge and Data Engineering.

[27]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[28]  John W. Sammon,et al.  An Optimal Set of Discriminant Vectors , 1975, IEEE Transactions on Computers.

[29]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[30]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[31]  Robert P. W. Duin,et al.  Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Cheng-Lin Liu,et al.  Evaluation of weighted Fisher criteria for large category dimensionality reduction in application to Chinese handwriting recognition , 2013, Pattern Recognit..

[33]  Ling Shao,et al.  Learning Computational Models of Video Memorability from fMRI Brain Imaging , 2015, IEEE Transactions on Cybernetics.

[34]  Wenming Zheng,et al.  Foley-Sammon optimal discriminant vectors using kernel approach , 2005, IEEE Trans. Neural Networks.

[35]  Xuelong Li,et al.  Two-Stage Learning to Predict Human Eye Fixations via SDAEs , 2016, IEEE Transactions on Cybernetics.

[36]  Lei Guo,et al.  Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Xintao Hu,et al.  Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping , 2016, Multidimens. Syst. Signal Process..

[38]  Feng Wu,et al.  Background Prior-Based Salient Object Detection via Deep Reconstruction Residual , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[39]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[40]  Hong Yan,et al.  Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data , 2008, IEEE Transactions on Knowledge and Data Engineering.

[41]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.