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Recognition of facial expressions using Gabor wavelets and learning vector quantization

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2008年第7期21卷 1056-1064页

作者： Bashyal, Shishir Venayagamoorthy, Ganesh K. Missouri Univ Sci & Technol Dept Elect & Comp Engn Real Time Power & Intelligent Syst Lab Rolla MO 65409 USA

Facial expression recognition has potential applications in different aspects of day-to-day life not yet realized due to absence of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction in combination with learning vector quantization (LVQ) for recognition of seven different facial expressions from still pictures of the human face. The results presented here are better in several aspects from earlier work in facial expression recognition. Firstly, it is observed that LVQ based feature classification technique proposed in this study performs better in recognizing fear expressions than multilayer perceptron (MLP) based classification technique used in earlier work. Secondly, this study indicates that the Japanese Female Facial Expression (JAFFE) database contains expressers that expressed expressions incorrectly and these incorrect images adversely affect the development of a reliable facial expression recognition system. By excluding the two expressers from the data set, an improvement in recognition rate from 87.51% to 90.22% has been achieved. The present study, therefore, proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and human computer interaction. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Facial expression recognition Gabor wavelets learning vector quantization JAFFE Principal component analysis

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Encoded pattern classification using constructive learning algorithms based on learning vector quantization

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NEURAL NETWORKS 1998年第2期11卷 315-322页

作者： Murthy, CNSG Venkatesh, YV Indian Inst Sci Dept Elect Engn Comp Vis & Artificial Intelligence Lab Bangalore 560012 Karnataka India

A novel encoding technique is proposed for the recognition of patterns using four different techniques for training artificial neural networks (ANNs) of the Kohonen type. Each template or model pattern is overlaid on a radial grid of appropriate size, and converted to a two-dimensional feature array which then acts as the training input to the ANN. The first technique employs Kohonen's self-organizing network, each neuron of which is assigned, after training, the label of the model pattern. It is found that a graphical plot of the labels of the neurons exhibits clusters (which means in effect that the feature array pertaining to distorted versions of the same pattern belongs to a specific cluster), thereby justifying the coding strategy used in this paper. When the new, unknown pattern is input to the network, it is classified to have the same label of the neuron whose corresponding model pattern is closest to the given pattern. In an attempt to reduce the computational time and the size of the network, and simultaneously improve accuracy in recognition, Kohonen's learning vector quantization (LVQ) algorithm is used to train the ANN. To further improve the network's performance and to realize a network of minimum size, two constructive learning algorithms, both based on LVQ, are proposed: (1) multi-step learning vector quantization (MLVQ), and (2) thermal multi-step learning vector quantization (TLVQ). When the proposed algorithms are applied to the classification of noiseless and noisy (and distorted) patterns, the results demonstrate that the pattern encoding strategy and the suggested training techniques for ANNs are efficient and robust. For lack of space, only the most essential results are presented here. For details, see Ganesh Murthy and Venkatesh (1996b). (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： Kohonen network learning learning vector quantization pattern classification pattern encoding self-organization

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Cursive character recognition by learning vector quantization

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PATTERN RECOGNITION LETTERS 2001年第6-7期22卷 625-629页

作者： Camastra, F Vinciarelli, A Epsag Spa I-16154 Genoa Italy IDIAP Inst Dalle Molle Intelligence Artif Percept CH-1920 Martigny Switzerland

This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: the first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49,000 isolated characters. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： cursive character recognition feature extraction cross-validation learning vector quantization

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Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

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EARTH SCIENCE INFORMATICS 2024年第6期17卷 5273-5288页

作者： Abbaszadeh, Maliheh Khosravi, Vahid Pour, Amin Beiranvand Univ Kashan Dept Min Engn Kashan Iran Czech Univ Life Sci Prague Fac Agrobiol Food & Nat Resources Dept Soil Sci & Soil Protect Kamycka 129 Prague 16500 Czech Republic German Res Ctr Geosci GFZ Sect Remote Sensing & Geoinformat 1 4 D-14473 Potsdam Germany Univ Malaysia Terengganu UMT Higher Inst Ctr Excellence HICoE Marine Sci Inst Oceanog & Environm INOS Kuala Nerus 21030 Malaysia

Geological domaining is an essential aspect of mineral resource evaluation. Various explicit and implicit modeling approaches have been developed for this purpose, but most of them are computationally expensive and complex, particularly when dealing with intricate mineralization systems and large datasets. Additionally, most of them require a time-consuming process for hyperparameter tuning. In this research, the application of the learning vector quantization (LVQ) classification algorithm has been proposed to address these challenges. The LVQ algorithm exhibits lower complexity and computational costs compared to other machine learning algorithms. Various versions of LVQ, including LVQ1, LVQ2, and LVQ3, have been implemented for geological domaining in the Darehzar porphyry copper deposit in southeastern Iran. Their performance in geological domaining has been thoroughly investigated and compared with the Support vector Machine (SVM), a widely accepted classification method in implicit domaining. The overall classification accuracy of LVQ1, LVQ2, LVQ3, and SVM is 90%, 90%, 91%, and 98%, respectively. Furthermore, the calculation time of these algorithms has been compared. Although the overall accuracy of the SVM method is similar to 7% higher, its calculation time is similar to 1000 times longer than LVQ methods. Therefore, LVQ emerges as a suitable alternative for geological domaining, especially when dealing with large datasets. In this study, the crucial significance of geological domaining in mineral resource evaluation is underscored, highlighting its vital role in comprehending mineralization systems and managing extensive *** research proposes the application of the LVQ classification algorithm as a solution to the challenges posed by computationally expensive and complex modeling approaches. LVQ is highlighted for its lower complexity and computational costs compared to other machine learning algorithms, positioning it as an efficient alternative.

关键词： learning vector quantization Support vector machines Geological domaining Classification Porphyry copper deposit

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Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2000年第5期11卷 1093-1105页

作者： Karayiannis, NB Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

关键词： aggregation operator clustering image segmentation learning vector quantization magnetic resonance (MR) imaging ordered weighted aggregation reformulation reformulation function

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An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 1997年第4期5卷 622-628页

作者： Karayiannis, NB Bezdek, JC UNIV W FLORIDA DEPT COMP SCI PENSACOLA FL 32514 USA

This letter derives a new interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization, These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning, Derivation of the new algorithms is accomplished by minimizing an average generalized distance between the feature vectors and prototypes using gradient descent, A close relationship between the resulting algorithms and fuzzy c-means is revealed by investigating the functionals involved. It is also shown that the fuzzy c-means and fuzzy learning vector quantization algorithms are related to the proposed algorithms if the learning rate at each iteration is selected to satisfy a certain condition.

关键词： batch LVQ algorithms competition function competitive learning fuzzy c-means fuzzy LVQ generalized average distance learning vector quantization

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learning vector quantization based predictor model selection for hourly load demand forecasting

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APPLIED SOFT COMPUTING 2022年 117卷

作者： Akarslan, Emre Afyon Kocatepe Univ Dept Elect Engn Afyon Turkey

The development of smart grids has enabled a wide variety of generation units to be included in the grid, which has made it necessary to predict the load demand to manage the grid accurately. In this study, a novel hybridization approach is proposed based on selecting the most appropriate method to be used in each prediction step. In this scope, learning vector quantization (LVQ) is employed as a classifier while Elman Neural Networks (ENN), and Ridge Regression are selected as predictors. In the first stage, a forecasting model is built and trained with ENN and Ridge Regression models on training data using 1-h before, 2-h before, and the first-order derivative of electricity consumption data. In the second stage, the method by which the most successful results are obtained for each forecast is determined and labeled. Then the LVQ model is built and trained to determine the most accurate forecast by using the same input employed in modeling. Finally, forecasts are performed by deciding which model to be used with LVQ. Experimental results show that more accurate forecasting performance is obtained with the proposed approach than the other two individual models. The different combinations of conventional models are used to illustrate the effectiveness of the proposed selection strategy, and experimental results show that better forecasting performance is obtained with the proposed approach than the individual ones at each combination.(c) 2022 Elsevier B.V. All rights reserved.

关键词： learning vector quantization Hourly load demand Hybrid model Predictor model selection

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Ordinal regression based on learning vector quantization

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NEURAL NETWORKS 2017年 93卷 76-88页

作者： Tang, Fengzhen Tino, Peter Chinese Acad Sci Shenyang Inst Automat 114 Nanta St Shenyang 110016 Liaoning Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted. Empirical investigation on a number of datasets reveals that overall the proposed approach tends to have superior out-of-sample performance, when compared to alternative ordinal regression methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ordinal regression learning vector quantization Generalized matrix learning vector quantization

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Differential privacy for learning vector quantization

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NEUROCOMPUTING 2019年 342卷 125-136页

作者： Brinkrolf, Johannes Goepfert, Christina Hammer, Barbara Bielefeld Univ CITEC Ctr Excellence Bielefeld Germany

Prototype-based machine learning methods such as learning vector quantisation (LVQ) offer flexible classification tools, which represent a classification in terms of typical prototypes. This representation leads to a particularly intuitive classification scheme, since prototypes can be inspected by a human partner in the same way as data points. Yet, it bears the risk of revealing private information included in the training data, since individual information of a single training data point can significantly influence the location of a prototype. In this contribution, we investigate the question how to algorithmically extend LVQ such that it provably obeys privacy constraints as offered by the notion of so-called differential privacy. More precisely, we demonstrate the sensitivity of LVQ to single data points and hence the need of its extension to private variants in case of possibly sensitive training data. We investigate three technologies which have been proposed in the context of differential privacy, and we extend these technologies to LVQ schemes. We investigate the effectiveness and efficiency of these schemes for various data sets, and we evaluate their scalability and robustness as regards the choice of meta-parameters and characteristics of training sets. Interestingly, one algorithm, which has been proposed in the literature due to its beneficial mathematical properties, does not scale well with data dimensionality, while two alternative techniques, which are based on simpler principles, display good results in practical settings. (C) 2019 Published by Elsevier B.V.

关键词： Differential privacy learning vector quantization

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LVQ-SMOTE - learning vector quantization based Synthetic Minority Over-sampling Technique for biomedical data

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BIODATA MINING 2013年第1期6卷 1-10页

作者： Nakamura, Munehiro Kajiwara, Yusuke Otsuka, Atsushi Kimura, Haruhiko Kanazawa Univ Dept Nat Sci & Engn Kanazawa Ishikawa 9200941 Japan Ritsumeikan Univ Marine Fac Informat Sci & Technol Kyoto Shiga 5258577 Japan

Background: Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets. Results: Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for beta-turn types prediction show some important patterns that have not been seen in previous analyses. Conclusions: The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

关键词： Biomedical data Over-sampling learning vector quantization Synthetic Minority Over-sampling Technique

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