The Experts below are selected from a list of 84588 Experts worldwide ranked by ideXlab platform
Igor Kheidorov - One of the best experts on this subject based on the ideXlab platform.
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A Novel GMM-Based Feature Reduction for Vocal Fold Pathology Diagnosis
Research Journal of Applied Sciences Engineering and Technology, 2013Co-Authors: Vahid Majidnezhad, Igor KheidorovAbstract:Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods (Support Vector Machines, Gaussian Mixture Model, etc.), the first and second stages play a critical role in performance and accuracy of the classification system. In this study we present initial study of Feature extraction and Feature Reduction in the task of vocal fold pathology diagnosis. A new type of Feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new method for Feature Reduction is proposed and compared with conventional methods such as Principal Component Analysis (PCA), F-Ratio and Fisher's discriminant ratio. Gaussian Mixture Model is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.
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The SVM-Based Feature Reduction in Vocal Fold Pathology Diagnosis
2013Co-Authors: Vahid Majidnezhad, Igor KheidorovAbstract:Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods (Support Vector Machines, Artificial Neural Networks, etc), the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of Feature extraction and Feature Reduction in the task of vocal fold pathology diagnosis. A new type of Feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new SVM-Based method for Feature Reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.
Tomaso Poggio - One of the best experts on this subject based on the ideXlab platform.
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hierarchical classification and Feature Reduction for fast face detection with support vector machines
Pattern Recognition, 2003Co-Authors: Bernd Heisele, Thomas Serre, Samuel Prentice, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use support vector machines as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level, a simple and fast linear classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. We propose a new method for automatically building and training a hierarchy of classifiers. In the second step we apply Feature Reduction to the top level classifier by choosing relevant image Features according to a measure derived from statistical learning theory. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance.
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Feature Reduction and hierarchy of classifiers for fast object detection in video images
Computer Vision and Pattern Recognition, 2001Co-Authors: Bernd Heisele, Thomas Serre, Shayan Mukherjee, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform Feature Reduction by choosing relevant image Features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.
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CVPR (2) - Feature Reduction and hierarchy of classifiers for fast object detection in video images
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1Co-Authors: Bernd Heisele, Thomas Serre, Shayan Mukherjee, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform Feature Reduction by choosing relevant image Features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.
Bernd Heisele - One of the best experts on this subject based on the ideXlab platform.
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hierarchical classification and Feature Reduction for fast face detection with support vector machines
Pattern Recognition, 2003Co-Authors: Bernd Heisele, Thomas Serre, Samuel Prentice, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use support vector machines as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level, a simple and fast linear classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. We propose a new method for automatically building and training a hierarchy of classifiers. In the second step we apply Feature Reduction to the top level classifier by choosing relevant image Features according to a measure derived from statistical learning theory. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance.
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Feature Reduction and hierarchy of classifiers for fast object detection in video images
Computer Vision and Pattern Recognition, 2001Co-Authors: Bernd Heisele, Thomas Serre, Shayan Mukherjee, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform Feature Reduction by choosing relevant image Features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.
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CVPR (2) - Feature Reduction and hierarchy of classifiers for fast object detection in video images
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1Co-Authors: Bernd Heisele, Thomas Serre, Shayan Mukherjee, Tomaso PoggioAbstract:We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform Feature Reduction by choosing relevant image Features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining Feature Reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.
Rene De Jesus Romero-troncoso - One of the best experts on this subject based on the ideXlab platform.
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Diagnosis methodology for identifying gearbox wear based on statistical time Feature Reduction
Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2017Co-Authors: Juan Jose Saucedo-dorantes, Miguel Delgado-prieto, Roque Alfredo Osornio-rios, Rene De Jesus Romero-troncosoAbstract:Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The Feature selection and Feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage Feature Reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage Feature Reduction approach involves a Feature selection and a Feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based Features. Second, a Feature selection is done by performing an analysis of the Fisher score. Third, a Feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.
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Diagnosis methodology for identifying gearbox wear based on statistical time Feature Reduction
Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2017Co-Authors: Juan Jose Saucedo-dorantes, Miguel Delgado-prieto, Roque Alfredo Osornio-rios, Rene De Jesus Romero-troncosoAbstract:Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The Feature selection and Feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage Feature Reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage Feature Reduction approach involves a Feature selection and a Feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based Features. Second, a Feature selection is done by performing an analysis of the Fisher score. Third, a Feature extraction is realized by mean...
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Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction
IEEE Transactions on Industry Applications, 2017Co-Authors: Juan Jose Saucedo-dorantes, Miguel Delgado-prieto, Roque Alfredo Osornio-rios, Rene De Jesus Romero-troncosoAbstract:Condition monitoring schemes are essential for increasing the reliability and ensuring the equipment efficiency in industrial processes. The Feature extraction and dimensionality Reduction are useful preprocessing steps to obtain high performance in condition monitoring schemes. To address this issue, this work presents a novel diagnosis methodology based on high-dimensional Feature Reduction applied to detect multiple faults in an induction motor linked to a kinematic chain. The proposed methodology involves a hybrid Feature Reduction that ensures a good processing of the acquired vibration signals. The method is performed sequentially. First, signal decomposition is carried out by means of empirical mode decomposition. Second, statistical-time-based Features are estimated from the resulting decompositions. Third, a Feature optimization is performed to preserve the data variance by a genetic algorithm in conjunction with the principal component analysis. Fourth, a Feature selection is done by means of Fisher score analysis. Fifth, a Feature extraction is performed through linear discriminant analysis. And, finally, sixth, the different considered faults are diagnosed by a Neural Network-based classifier. The performance and the effectiveness of the proposed diagnosis methodology is validated experimentally and compared with classical Feature Reduction strategies, making the proposed methodology suitable for industry applications.
Long Thanh Ngo - One of the best experts on this subject based on the ideXlab platform.
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Feature-Reduction fuzzy co-clustering approach for hyper-spectral image analysis
Knowledge-Based Systems, 2021Co-Authors: Nha Van Pham, Witold Pedrycz, Long Thanh NgoAbstract:Abstract Fuzzy co-clustering algorithms are the effective techniques for multi-dimensional clustering in which all Features are considered of equal importance (relevance). In fact, the Features’ importance could be different, even several of them could be considered redundant. The removal of the redundant Features has formed the idea of Feature-Reduction in problems of the big data processing. In this paper, we propose a new unsupervised learning scheme by incorporating the Feature-weighted entropy into the objective function of fuzzy co-clustering, called the Feature-Reduction Fuzzy Co-Clustering Algorithm (FRFCoC). First, a new objective function is formed on the basis of the original fuzzy co-clustering objective function which adds parameters representing the entropy weight of the different Features. Next, a Feature-Reduction and clustering automatic schema are adjusted based on FCoC’s original learning schema which calculates new parameters and conditions to eliminate irrelevant Feature components. FRFCoC algorithm can be mathematically shown to converge after a finite number of iterations. The experiment results were conducted on some many-Features data sets and hyperspectral images that have demonstrated the outstanding performance of FRFCoC algorithm compared with some previously proposed algorithms.
