Abstract This paper proposes a novel optimization program for solving the Robust Principle Compon... more Abstract This paper proposes a novel optimization program for solving the Robust Principle Component Analysis (RPCA) problem, which decomposes a data matrix into a conventional low-rank part plus a particular block-sparse residual. This kind of block-sparse residual often scattered in the source signal scope as contaminants, and often existed in many practical applications, such as an ordinary imaging system, a Hyper Spectral Imaging system, EEG and MEG, and types of physiological signals. Different from most currently existing approaches, the study perceived especially a highly spatial correlation among the inner structure of the neighbouring pixels in this contiguously block-sparse residual. The high intra-block correlation is then introduced as prior information to deal the governing optimization problem. In order to enhance the block-sparsity and maintain the local smoothness simultaneously, a localized low-rank promoting method is introduced with a theoretical guarantee. An efficient solving algorithm is designed accordingly with a convergence analysis by adopting the classical Alternating Direction Method of Multipliers (ADMM) framework. In addition to the theoretical model derivation, several synthetic simulations together with a real application on image denoising experiment have been conducted to validate the proposed model. As expected, the models outperforms significantly the compared state-of-the-arts.
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
In this paper, we develop a novel coordinate descent fuzzy twin SVM (CDFTSVM) for classification.... more In this paper, we develop a novel coordinate descent fuzzy twin SVM (CDFTSVM) for classification. The proposed CDFTSVM not only inherits the advantages of twin SVM but also leads to a rapid and robust classification results. Specifically, our CDFTSVM has two distinguished advantages: (1) An effective fuzzy membership function is produced for removing the noise incurred by the contaminant inputs. (2) A coordinate descent strategy with shrinking by active set is used to deal with the computational complexity brought by the high dimensional input. In addition, a series of simulation experiments are conducted to verify the performance of the CDFTSVM, which further supports our previous claims.
A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-se... more A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly en...
2018 International Automatic Control Conference (CACS), 2018
Due to gradually aging lifespan of elders in human society, a lot of new demands have been brough... more Due to gradually aging lifespan of elders in human society, a lot of new demands have been brought to facilitate their daily life. The elder driving assistive technology is one of the demands. The elder driving assistive technology is not only beneficial in increasing safety of the driver and the people around his vehicle, but also advantageous in decreasing the public cost of society safety. Thus, the study set a goal to pursue a driving assistive apparatus which can respond immediately to the elder a helpful warning or arrestment during a potential accident crisis. The apparatus dynamically monitors the vehicle movement and it corresponding diver operation mode, and detects the irregularity between the driver and his vehicle. Sensory devices for detecting the behaviors were installed in the vehicle, including imaging camera, inertial measurement unit, Lidar scanner, steering wheel angle sensor, depression sensors on accelerator pedal and brake pedal to form a sensory network for c...
ABSTRACT The purpose of this paper is to introduce a concept of fuzzy class memberships to the sa... more ABSTRACT The purpose of this paper is to introduce a concept of fuzzy class memberships to the samples of training set in the support vector classifier. The inclusion of fuzzy values contributed a set of dynamic Lagrangian constraints, which setups a more specific space for searching the optimum, and conducted a more accurate classification performance. The developed model stepped into the sub-structure of the classifier, and involved the complex micro-interactions among the training samples to form a more precise separating hyperplane by fuzzy membership. The micro-interactions also altered the hyperplane and its corresponding margin, and achieved the deep-reaching classification accuracy around the sub-optimal region.
ABSTRACT Using class label fuzzification, this study develops the idea of refreshing the attitude... more ABSTRACT Using class label fuzzification, this study develops the idea of refreshing the attitude of the difficult training examples and gaining a more robust classifier for large-margin support vector machines (SVMs). Fuzzification relaxes the specific hard-limited Lagrangian constraints of the difficult examples, extends the infeasible space of the canonical constraints for optimization, and reconfigures the consequent decision function with a wider margin. With the margin, a classifier capable of achieving a high generalization performance can be more robust. This paper traces the rationale for such a robust performance back to the changes of governing loss function. From the aspect of loss function, the reasons are causally explained. In the study, we also demonstrate a two-stage system for experiments to show the changes corresponding to the label fuzzification. The system first captures the difficult examples in the first-stage preprocessor, and assigns them various fuzzified class labels. Three types of membership functions, including a constant, a linear, and a sigmoidal membership function, are designated in the preprocessor to manipulate the within-class correlations of the difficult examples for reference of the fuzzification. The consequent performance benchmarks confirm the robust and generalized ability due to the label fuzzification. Since the change of yi′ is fundamental, the idea may be transplanted to different prototypes of SVM.
ABSTRACT An alternative robotic grasping gripper including a vision system, machine fingers, pres... more ABSTRACT An alternative robotic grasping gripper including a vision system, machine fingers, pressure modules, and smart fuzzy grasping controller is designed and implemented in this paper. To avoid the redundant computation of inverse kinematics, the relative coordinates are adopted in the proposed architecture. To identify the stiffness and shape of different grasping objects, a smart fuzzy grasping controller is embedded into the recognition process first. According to the identifying results, the membership functions of the smart fuzzy grasping controller are precisely tuned to generate the joint angles of the servo motors online. The effectiveness is verified by some experimental results, and the proposed architectures are implemented in the home-made robotic grasping gripper in laboratory.
2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), 2012
ABSTRACT A navigation system based on support vector machine (SVM) path planning and fuzzy slidin... more ABSTRACT A navigation system based on support vector machine (SVM) path planning and fuzzy sliding-mode controlled (SMC) path follower is developed for wheeled agent in this paper. The developed system, comprising image acquisition, map formatting, path planning, label assignment and path tracking inference mechanism, aims to gracefully follow a planned smooth path. In the first portion, a Voronoi diagram is employed as a preprocessor to roughly fit a safe route between the initial and goal positions with discontinuous fragmental segments to avoid obstacles. A postprocessor of Gaussian kernel SVM is then applied to consecutively convert the fragmental path to a differentiable continuous path for a safe and smooth passage off line. In the second portion, a path tracking inference mechanism based on fuzzy SMC is proposed subsequently to track the planned path on line. In the study, a practical validation framework is implemented to assess the performance of the proposed system. Beside the two-stage path planner and the fuzzy SMC path tracking inference mechanism, the framework devises further an image processing to precondition the inputs of the system. With the real devised system, a series of experiments are carried out and analyzed to confirm the expected performance. The experiments show a robust capability of the system for both path planning and path tracking under various obstacle layouts.
Robustness is an important characteristic of a classifier. With higher robustness, a classifier c... more Robustness is an important characteristic of a classifier. With higher robustness, a classifier can resist much more against the noise of a contaminated dataset which is usually occurred in the real-world applications. With its excellence in robustness, a fuzzy support vector machine (fuzzy SVM) developed by Lin and Wang deserves the most attention among varieties of support vector machines. The main goal of this paper is to gain the Joachims’ ξ-α bound of the fuzzy SVM. Based on the decoupled α and ξ terms in its expression, the ξ-α based estimation is particularly suitable for robustness analysis of the fuzzy SVM. The study re-examines the theory of the fuzzy SVM having an additional fuzzy input si in details with the ξ-α estimation, and conducts a relatively contracted condition for upper bounding the corresponding performance. The bound confirms the crucial robustness which the fuzzy SVM can achieve analytically, and would be helpful for the works such as model selection or mode...
Abstract This paper proposes a novel optimization program for solving the Robust Principle Compon... more Abstract This paper proposes a novel optimization program for solving the Robust Principle Component Analysis (RPCA) problem, which decomposes a data matrix into a conventional low-rank part plus a particular block-sparse residual. This kind of block-sparse residual often scattered in the source signal scope as contaminants, and often existed in many practical applications, such as an ordinary imaging system, a Hyper Spectral Imaging system, EEG and MEG, and types of physiological signals. Different from most currently existing approaches, the study perceived especially a highly spatial correlation among the inner structure of the neighbouring pixels in this contiguously block-sparse residual. The high intra-block correlation is then introduced as prior information to deal the governing optimization problem. In order to enhance the block-sparsity and maintain the local smoothness simultaneously, a localized low-rank promoting method is introduced with a theoretical guarantee. An efficient solving algorithm is designed accordingly with a convergence analysis by adopting the classical Alternating Direction Method of Multipliers (ADMM) framework. In addition to the theoretical model derivation, several synthetic simulations together with a real application on image denoising experiment have been conducted to validate the proposed model. As expected, the models outperforms significantly the compared state-of-the-arts.
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
In this paper, we develop a novel coordinate descent fuzzy twin SVM (CDFTSVM) for classification.... more In this paper, we develop a novel coordinate descent fuzzy twin SVM (CDFTSVM) for classification. The proposed CDFTSVM not only inherits the advantages of twin SVM but also leads to a rapid and robust classification results. Specifically, our CDFTSVM has two distinguished advantages: (1) An effective fuzzy membership function is produced for removing the noise incurred by the contaminant inputs. (2) A coordinate descent strategy with shrinking by active set is used to deal with the computational complexity brought by the high dimensional input. In addition, a series of simulation experiments are conducted to verify the performance of the CDFTSVM, which further supports our previous claims.
A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-se... more A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly en...
2018 International Automatic Control Conference (CACS), 2018
Due to gradually aging lifespan of elders in human society, a lot of new demands have been brough... more Due to gradually aging lifespan of elders in human society, a lot of new demands have been brought to facilitate their daily life. The elder driving assistive technology is one of the demands. The elder driving assistive technology is not only beneficial in increasing safety of the driver and the people around his vehicle, but also advantageous in decreasing the public cost of society safety. Thus, the study set a goal to pursue a driving assistive apparatus which can respond immediately to the elder a helpful warning or arrestment during a potential accident crisis. The apparatus dynamically monitors the vehicle movement and it corresponding diver operation mode, and detects the irregularity between the driver and his vehicle. Sensory devices for detecting the behaviors were installed in the vehicle, including imaging camera, inertial measurement unit, Lidar scanner, steering wheel angle sensor, depression sensors on accelerator pedal and brake pedal to form a sensory network for c...
ABSTRACT The purpose of this paper is to introduce a concept of fuzzy class memberships to the sa... more ABSTRACT The purpose of this paper is to introduce a concept of fuzzy class memberships to the samples of training set in the support vector classifier. The inclusion of fuzzy values contributed a set of dynamic Lagrangian constraints, which setups a more specific space for searching the optimum, and conducted a more accurate classification performance. The developed model stepped into the sub-structure of the classifier, and involved the complex micro-interactions among the training samples to form a more precise separating hyperplane by fuzzy membership. The micro-interactions also altered the hyperplane and its corresponding margin, and achieved the deep-reaching classification accuracy around the sub-optimal region.
ABSTRACT Using class label fuzzification, this study develops the idea of refreshing the attitude... more ABSTRACT Using class label fuzzification, this study develops the idea of refreshing the attitude of the difficult training examples and gaining a more robust classifier for large-margin support vector machines (SVMs). Fuzzification relaxes the specific hard-limited Lagrangian constraints of the difficult examples, extends the infeasible space of the canonical constraints for optimization, and reconfigures the consequent decision function with a wider margin. With the margin, a classifier capable of achieving a high generalization performance can be more robust. This paper traces the rationale for such a robust performance back to the changes of governing loss function. From the aspect of loss function, the reasons are causally explained. In the study, we also demonstrate a two-stage system for experiments to show the changes corresponding to the label fuzzification. The system first captures the difficult examples in the first-stage preprocessor, and assigns them various fuzzified class labels. Three types of membership functions, including a constant, a linear, and a sigmoidal membership function, are designated in the preprocessor to manipulate the within-class correlations of the difficult examples for reference of the fuzzification. The consequent performance benchmarks confirm the robust and generalized ability due to the label fuzzification. Since the change of yi′ is fundamental, the idea may be transplanted to different prototypes of SVM.
ABSTRACT An alternative robotic grasping gripper including a vision system, machine fingers, pres... more ABSTRACT An alternative robotic grasping gripper including a vision system, machine fingers, pressure modules, and smart fuzzy grasping controller is designed and implemented in this paper. To avoid the redundant computation of inverse kinematics, the relative coordinates are adopted in the proposed architecture. To identify the stiffness and shape of different grasping objects, a smart fuzzy grasping controller is embedded into the recognition process first. According to the identifying results, the membership functions of the smart fuzzy grasping controller are precisely tuned to generate the joint angles of the servo motors online. The effectiveness is verified by some experimental results, and the proposed architectures are implemented in the home-made robotic grasping gripper in laboratory.
2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), 2012
ABSTRACT A navigation system based on support vector machine (SVM) path planning and fuzzy slidin... more ABSTRACT A navigation system based on support vector machine (SVM) path planning and fuzzy sliding-mode controlled (SMC) path follower is developed for wheeled agent in this paper. The developed system, comprising image acquisition, map formatting, path planning, label assignment and path tracking inference mechanism, aims to gracefully follow a planned smooth path. In the first portion, a Voronoi diagram is employed as a preprocessor to roughly fit a safe route between the initial and goal positions with discontinuous fragmental segments to avoid obstacles. A postprocessor of Gaussian kernel SVM is then applied to consecutively convert the fragmental path to a differentiable continuous path for a safe and smooth passage off line. In the second portion, a path tracking inference mechanism based on fuzzy SMC is proposed subsequently to track the planned path on line. In the study, a practical validation framework is implemented to assess the performance of the proposed system. Beside the two-stage path planner and the fuzzy SMC path tracking inference mechanism, the framework devises further an image processing to precondition the inputs of the system. With the real devised system, a series of experiments are carried out and analyzed to confirm the expected performance. The experiments show a robust capability of the system for both path planning and path tracking under various obstacle layouts.
Robustness is an important characteristic of a classifier. With higher robustness, a classifier c... more Robustness is an important characteristic of a classifier. With higher robustness, a classifier can resist much more against the noise of a contaminated dataset which is usually occurred in the real-world applications. With its excellence in robustness, a fuzzy support vector machine (fuzzy SVM) developed by Lin and Wang deserves the most attention among varieties of support vector machines. The main goal of this paper is to gain the Joachims’ ξ-α bound of the fuzzy SVM. Based on the decoupled α and ξ terms in its expression, the ξ-α based estimation is particularly suitable for robustness analysis of the fuzzy SVM. The study re-examines the theory of the fuzzy SVM having an additional fuzzy input si in details with the ξ-α estimation, and conducts a relatively contracted condition for upper bounding the corresponding performance. The bound confirms the crucial robustness which the fuzzy SVM can achieve analytically, and would be helpful for the works such as model selection or mode...
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Papers by Chan-Yun Yang