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Statistical pattern recognition: A review
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
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The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the wellknown methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Least square incremental linear discriminant analysis
 In Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, ICDM ’09
, 2009
"... Abstract—Linear discriminant analysis (LDA) is a wellknown dimension reduction approach, which projects highdimensional data into a lowdimensional space with the best separation of different classes. In many tasks, the data accumulates over time, and thus incremental LDA is more desirable than b ..."
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Abstract—Linear discriminant analysis (LDA) is a wellknown dimension reduction approach, which projects highdimensional data into a lowdimensional space with the best separation of different classes. In many tasks, the data accumulates over time, and thus incremental LDA is more desirable than batch LDA. Several incremental LDA algorithms have been developed and achieved success; however, the eigenproblem involved requires a large computation cost, which hampers the efficiency of these algorithms. In this paper, we propose a new incremental LDA algorithm, LSILDA, based on the least square solution of LDA. When new samples are received, LSILDA incrementally updates the least square solution of LDA. Our analysis discloses that this algorithm produces the exact least square solution of batch LDA, while its computational cost is O(min(n, d) × d) for one update on dataset containing n instances in ddimensional space. Experimental results show that comparing with stateoftheart incremental LDA algorithms, our proposed LSILDA achieves high accuracy with low time cost. KeywordsDimension reduction; linear discriminant analysis (LDA); incremental learning; least square I.
An incremental subspace learning algorithm to categorize large scale text data
 In APWeb
, 2005
"... Abstract. The dramatic growth in the number and size of online information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Interclass Scatter (IIS) algo ..."
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Abstract. The dramatic growth in the number and size of online information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Interclass Scatter (IIS) algorithm. Unlike traditional batch learners, IIS learns from a stream of training data, not a set. IIS overcomes the inherent problem of some other incremental operations such as Incremental Principal Component Analysis (PCA) and Incremental Linear Discriminant Analysis (LDA). The experimental results on the synthetic datasets show that IIS performs as well as LDA and is more robust against noise. In addition, the experiments on the Reuters Corpus Volume 1 (RCV1) dataset show that IIS outperforms stateoftheart Incremental Principal Component Analysis (IPCA) algorithm, a related algorithm, and Information Gain in efficiency and effectiveness respectively. 1
Algorithms and networks for accelerated convergence of adaptive LDA
 PATTERN RECOGNITION
, 2004
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IMPLEMENTATION OF PATTERN RECOGNITION TECHNIQUES AND OVERVIEW OF ITS APPLICATIONS IN VARIOUS AREAS OF ARTIFICIAL INTELLIGENCE
"... A pattern is an entity, vaguely defined, that could be given a name, e.g. fingerprint image, handwritten word, human face, speech signal, DNA sequence. Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and m ..."
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A pattern is an entity, vaguely defined, that could be given a name, e.g. fingerprint image, handwritten word, human face, speech signal, DNA sequence. Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. The goal of pattern recognition research is to clarify complicated mechanisms of decision making processes and automatic these function using computers. Pattern recognition systems can be designed using the following main approaches: template matching, statistical methods, syntactic methods and neural networks. This paper reviews Pattern Recognition, Process, Design Cycle, Application, Models etc. This paper focuses on Statistical method of pattern Recognition.
COLOR IMAGES SEGMENTATION USING A SELFORGANIZING NETWORK WITH ADAPTIVE LEARNING RATE
"... In this paper, an approach based on selforganizing neural network with adaptive learning rate for color image segmentation is presented. It is wellknown that the training speed depends on the choice of the learning rate. If the learning rate is small, the learning process is stable but at the expe ..."
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In this paper, an approach based on selforganizing neural network with adaptive learning rate for color image segmentation is presented. It is wellknown that the training speed depends on the choice of the learning rate. If the learning rate is small, the learning process is stable but at the expense of computation time. If the learning rate is too large, the estimation of the weights may diverge. The usual methods for this purpose are based on constant learning rate and because of its inflexibility; the cluster result is not so satisfactory. In this approach, self organized neural network has adaptive learning rate. It results in better convergence with equal quality compared to last works in this field. In simulation, training of the network using this approach is much faster than using a constant learning rate.
Incremental Constrained Discriminant Component Analysis
"... Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popularity. However, this algorithm is not applicable in the environment with large amount of data points or when the data point arrive in a sequential manner. In this paper, we aim to propose an incrementa ..."
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Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popularity. However, this algorithm is not applicable in the environment with large amount of data points or when the data point arrive in a sequential manner. In this paper, we aim to propose an incremental version of this algorithm called Incremental Constrained Discriminant Component Analysis (ICDCA) to reduce the computational cost of this algorithm in large datasets. The ICDCA updates the within class scatter matrix and between class scatter matrix instead of calculating it from scratch. This change significantly reduces the computational cost of feature extraction process while keep the accuracy of such features as close as possible to offline version of this algorithm. In the end the effectiveness of ICDCA is compared to other recently proposed incremental LDA. To ensure the reliability of these experiments, they are repeated with several UC I data set. In these comparisons, advantage of ICDCA in the accuracy and speed is demonstrated.
A New Incremental Optimal Feature Extraction Method for Online Applications
"... Abstract. In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adaptive algorithms for the computation of the square root of the inverse covar ..."
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Abstract. In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adaptive algorithms for the computation of the square root of the inverse covariance matrix 21−Σ are introduced. The proof for the convergence of the new adaptive algorithm is given by presenting the related cost function and discussing about its initial conditions. The new adaptive algorithms are used before an adaptive principal component analysis algorithm in order to construct an adaptive multivariate multiclass LDA algorithm. Adaptive nature of the new optimal feature extraction method makes it appropriate for online pattern recognition applications. Both adaptive algorithms in the proposed structure are trained simultaneously, using a stream of input data. Experimental results using synthetic and real multiclass multidimensional sequence of data, demonstrated the effectiveness of the new adaptive feature extraction algorithm.
G IT
, 2007
"... Abstract: Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in biometric feature extractio ..."
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Abstract: Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in biometric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this article, we briefly survey two kinds of major applications of CI in biometric technologies, CIbased feature extraction and CIbased biometric matching. Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. CIbased methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. CIbased biometric technologies are powerful when used in the representation and recognition of incomplete biometric data, discriminative feature extraction, biometric matching, and online template updating, and promise to have an important role in the future development of biometric technologies.