Daming Shi

Handwritten Chinese radical recognition using nonlinear active shape models

Handwritten Chinese characters can be recognized by first extracting the basic shapes (radicals) of which they are composed. Radicals are described by nonlinear active shape models and optimal parameters found using the chamfer distance transform and a dynamic tunneling algorithm. The radical recognition rate is 96.5 percent correct (writer-independent) on 280,000 characters containing 98 radical classes.

Sensitivity analysis applied to the construction of radial basis function networks

Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the outputs sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.

FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning

As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy CMAC, in which Bayesian Ying-Yang (BYY) learning is introduced to determine the optimal fuzzy sets, and a truth-value restriction inference scheme is subsequently employed to derive the truth values of the rule weights of implication rules. The BYY is motivated from the famous Chinese ancient Ying-Yang philosophy: everything in the universe can be viewed as a product of a constant conflict between opposites-Ying and Yang, a perfect status is reached when Ying and Yang achieve harmony. The proposed fuzzy CMAC (FCMAC)-BYY enjoys the following advantages. First, it has a higher generalization ability because the fuzzy rule sets are systematically optimized by BYY; second, it reduces the memory requirement of the network by a significant degree as compared to the original CMAC; and third, it provides an intuitive fuzzy logic reasoning and has clear semantic meanings. The experimental results on some benchmark datasets show that the proposed FCMAC-BYY outperforms the existing representative techniques in the research literature

Fingerprint minutiae matching using the adjacent feature vector

Minutiae matching is the most popular approach to fingerprint verification. In this paper, we propose a novel fingerprint feature named the adjacent feature vector (AFV) for fingerprint matching. An AFV consists of four adjacent relative orientations and six ridge counts of a minutia. Given a fingerprint image, the optimal matching score is computed in three stages: (1) minutiae candidate pairs searching based on AFVs; (2) coordinate transform for image rotation and translation; and (3) transformed minutiae matching to get matching score. The experimental results show that the proposed method provides a good trade-off between speed and accuracy.

Adjacent orientation vector based fingerprint minutiae matching system

Minutia matching is the most popular approach to fingerprint recognition. We analyzed a novel fingerprint feature named adjacent orientation vector, or AOV, for fingerprint matching. In the first stage, AOV is used to find possible minutiae pairs. Then one minutiae set is rotated and translated. This is followed by a preliminary matching to ensure reliability as well as a fine matching to overcome possible distortion. Such method has been deployed to a payroll and security access information system and its workability is encouraging. The information system aims to offer a highly secured and automated identification system for payroll tracking as well as authorized access to working areas.

LRSR: Low-Rank-Sparse representation for subspace clustering

Abstract High-dimensional data in the real world often resides in low-dimensional subspaces. The state-of-the-art methods for subspace segmentation include Low Rank Representation (LRR) and Sparse Representation (SR). The former seeks the global lowest rank representation but restrictively assumes the independence among subspaces, whereas the latter seeks the clustering of disjoint or overlapped subspaces through locality measure, which may cause failure in the case of large noises. To this end, a Low Rank subspace Sparse Representation framework, hereafter referred to as LRSR, is proposed in this paper to recover and segment embedding subspaces simultaneously. Three major contributions can be claimed in this paper: First, a clean dictionary is constructed by optimizing its nuclear norm, low-rank-sparse coefficient matrix obtained using linearized alternating direction method (LADM). Second, both the convergence proof and the complexity analysis are given to prove the effectiveness and efficiency of our proposed LRSR algorithm. Third, the experiments on synthetic data and two benchmark datasets further verify that the LRSR enjoys the capability of clustering disjoint subspaces as well as the robustness against large noises, thanks to its considerations of both global and local subspace information. Therefore, it has been demonstrated in this research that our proposed LRSR algorithm outperforms the state-of-the-art subspace clustering methods, verified by both theoretical analysis and the empirical studies.

A radical approach to handwritten Chinese character recognition using active handwriting models

This paper applies active handwriting models (AHM) to handwritten Chinese character recognition. Exploiting active shape models (ASM), the AHM can capture the handwriting variation from character skeletons. The AHM has the following characteristics: principal component analysis is applied to capture variations caused by handwriting, an energy functional on the basis of chamfer distance transform is introduced as a criterion to fit the model to a target character skeleton, and the dynamic tunneling algorithm (DTA) is incorporated with gradient descent to search for shape parameters. The AHM is used within a radical approach to handwritten Chinese characters recognition, which converts the complex pattern recognition problem to recognizing a small set of primitive structures-radicals. Our initial experiments are conducted on 98 radicals covering 1400 loosely-constrained Chinese character categories written by 200 different writers. The correct matching rate is 94.2% on these 2.8/spl times/10/sup 5/ characters. Comparison with existing radical approaches shows that our method achieves superior performance.

Sparse Kernel Learning with LASSO and Bayesian Inference Algorithm

Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In International conference on artificial intelligence and statistics (pp. 580-587). San Juan, Puerto Rico: MIT Press]. This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages.

Product Demand Forecasting with a Novel Fuzzy CMAC

Forecasting product demand has always been a crucial challenge for managers as they play an important role in making many business critical decisions such as production and inventory planning. These decisions are instrumental in meeting customer demand and ensuring the survival of the organization. This paper introduces a novel Fuzzy Cerebellar-Model-Articulation-Controller (FCMAC) with a Truth Value Restriction (TVR) inference scheme for time-series forecasting and investigates its performance in comparison to established techniques such as the Single Exponential Smoothing, Holt’s Linear Trend, Holt-Winter’s Additive methods, the Box-Jenkin’s ARIMA model, radial basis function networks, and multi-layer perceptrons. Our experiments are conducted on the product demand data from the M3 Competition and the US Census Bureau. The results reveal that the FCMAC model yields lower errors for these data sets. The conditions under which the FCMAC model emerged significantly superior are discussed.

Significant vector learning to construct sparse kernel regression models

A novel significant vector (SV) regression algorithm is proposed in this paper based on an analysis of Chens orthogonal least squares (OLS) regression algorithm. The proposed regularized SV algorithm finds the significant vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing the orthogonalization needed in the OLS algorithm.