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Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition

IET BIOMETRICS 2014年第4期3卷 278-290页

作者： Wang, Faqiang Zhang, Hongzhi Wang, Kuanquan Zuo, Wangmeng Harbin Inst Technol Computat Percept & Cognit Ctr Sch Comp Sci & Technol Harbin 150001 Peoples R China

For the robust recognition of noisy face images, in this study, the authors improved the fast neighbourhood component analysis (FNCA) model by introducing a novel spatially smooth regulariser (SSR), resulting in the FNCA-SSR model. The SSR can enforce local spatial smoothness by penalising large differences between adjacent pixels, and makes FNCA-SSR model robust against noise in face image. Moreover, the gradient of SSR can be efficiently computed in image space, and thus the optimisation problem of FNCA-SSR can be conveniently solved by using the gradient descent algorithm. Experimental results on several face data sets show that, for the recognition of noisy face images, FNCA-SSR is robust against Gaussian noise and salt and pepper noise, and can achieve much higher recognition accuracy than FNCA and other competing methods.

关键词： face recognition gradient methods statistical analysis fast neighbourhood component analysis spatially smooth regulariser robust noisy face recognition FNCA-SSR model local spatial smoothness adjacent pixels optimisation problem gradient descent algorithm face data sets Gaussian noise salt and pepper noise

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Extended content-boosted matrix factorization algorithm for recommender systems 18th

Extended content-boosted matrix factorization algorithm for ...

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18th Annual International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Krasnoshchok, Oleksandr Lamo, Yngve Bergen Univ Coll N-5020 Bergen Norway

Recommender technologies have been developed to give helpful predictions for decision making under uncertainty. An extensive amount of research has been done to increase the quality of such predictions, currently the methods based on matrix factorization are recognized as one of the most efficient. The focus of this paper is to extend a matrix factorization algorithm with content awareness to increase prediction accuracy. A recommender system prototype based on the resulting Extended Content-Boosted Matrix Factorization algorithm is designed, developed and evaluated. The algorithm has been evaluated by empirical evaluation, which starts with creating of an experimental design, then conducting off-line empirical tests with accuracy measurement. The result revealed further potential of the content awareness in matrix factorization methods, which has not been fully realized in the generalized alignment-biased algorithm by Nguyen and Zhu and uncovers opportunities for future research. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： gradient descent algorithm Content Aware Collaborative Filtering algorithm

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Medical Diagnosis Applications Using a Novel Interactively Recurrent Self-evolving Fuzzy CMAC Model

Medical Diagnosis Applications Using a Novel Interactively R...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Jyun-Guo Tai, Shen-Chuan Lin, Cheng-Jian Natl Cheng Kung Univ Inst Comput & Commun Engn Tainan 701 Taiwan Natl Chin Yi Univ Technol Dept Comput Sci & Inf Engn Taichung Taiwan

ISBN: (纸本)9781479914845

In this paper, a recurrent self-evolving Fuzzy Cerebellar Model Articulation Controller (FCMAC) model for classification problems is developed, namely the interactively recurrent self-evolving fuzzy Cerebellar Model Articulation Controller (IRSFCMAC). The interactively recurrent structure in an IRSFCMAC is formed as external loops and internal feedbacks by feeding the rule firing strength to itself and others rules. The IRSFCMAC learning starts with an empty rule base and all of rules are generated and learned online, through a simultaneous structure and parameter learning, while the relative parameters are learned through a gradient descent algorithm. The proposed IRSFCMAC is tested by the four benchmarked classification problems and compared with the well-known traditional FCMAC. Experimental results show that the proposed IRSFCMAC model enhanced classification performance results, in terms of accuracy and RMSE.

关键词： interactively recurrent self-evolving fuzzy Cerebellar Model Articulation Controller gradient descent algorithm

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A NOVEL SPATIALLY CONSTRAINED MIXTURE MODEL FOR IMAGE SEGMENTATION 19

A NOVEL SPATIALLY CONSTRAINED MIXTURE MODEL FOR IMAGE SEGMEN...

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19th International Conference on Digital Signal Processing (DSP)

作者： Xiao, Zhiyong Yuan, Yunhao Yang, Jinlong Ge, Hongwei Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China

ISBN: (纸本)9781479946129

We present a novel spatially constrained mixture model for image segmentation. This model assumes that the prior distribution for each pixel depends on its neighboring pixels', and the degree of dependency is decided by the geometric closeness. The negative log-likelihood function of the proposed method is viewed as energy function, and the parameters of the energy function are estimated by gradient descent algorithm. Evaluation of the developed method is done on synthetic and real world images. Experimental results are compared with those obtained using mixture model-based methods. The proposed approach performs better than other ones in terms of classification accuracy.

关键词： Mixture model spatial constraint energy function gradient descent algorithm image segmentation

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ANN Approach for Magnetic Levitation Stabilization Using gradient and Quasi Newton Learning

ANN Approach for Magnetic Levitation Stabilization Using Gra...

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Students Conference on Engineering and Systems (SCES)

作者： Saini, Arun Kumar Sharma, Veena NIT Dept Elect Engn Hamirpur 177005 HP India

ISBN: (纸本)9781479949397

Magnetic Levitation (Maglev) stabilization has been the area of interest for various engineering fields. Classical controllers (like PID) can handle linear plants easily but when the plants are non-linear they have difficulties to deal with. This paper presents the Neural Network controller that are nonlinear in nature and can handle the controlling of nonlinear plants. The gradient descent algorithm is used to minimize the error function. Further the results of NN (Neural Network) controller are improved using Conjugate gradient learning and Quasi Newton methods. The results are presented to show better tracking behavior after applying different learning algorithms.

关键词： ANN (Artificial Neural Network) BGFS (Broyden Fletcher Goldfarb Shanno) algorithm Fletcher Reeves algorithm gradient descent algorithm Levenberg Marquardt algorithm

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An adaptive fuzzy PI control for a class of unknown nonaffine systems

An adaptive fuzzy PI control for a class of unknown nonaffin...

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International Conference on Control, Engineering & Information Technology

作者： Nidhal CHERRAT Hamid BOUBERTAKH Laboratoire d'Automatique de Jijel-LAJ University of Jijel

ISBN: (纸本)9781479982134

In this work a fuzzy adaptive PI controller is proposed for a class of uncertain nonaffine nonlinear systems. The parameters of the PI controller that can achieve the control objectives are approximated by a set of fuzzy systems. The fuzzy systems' parameters are adjusted by the gradient descent algorithm. The purpose behind the use of this adaptation algorithm is to minimize the error between the unknown ideal PI controller and the fuzzy PI controller. The stability of the closed-loop system is proven analytically based on Lyapunov approach. A simulation example is presented to illustrate the performance of the proposed scheme.

关键词： Adaptive PI controller Fuzzy systems Nonaffine nonlinear systems gradient descent algorithm Lyapunov stability

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Parallel tracing of multiple trajectories in gradient descent algorithm with Cell Broadband Engine 09

Parallel tracing of multiple trajectories in gradient descen...

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Proceedings of the 2009 Spring Simulation Multiconference

作者： Yuri Boiko Gabriel Wainer Carleton University Ottawa ON Canada

Explored here is the ability of Cell B.E. to efficiently reveal viable solutions of nonlinear function approximation with multilayer perceptron (MLP) employing gradient descent algorithm. The capacity of Cell BE to asynchronously trace several trajectories of implemented gradient descent algorithm from random set of starting points offers advantage of revealing statistical trends and classifying viable optimal approximations delivered by simulated function generator. Approximation conditions of surfaces of 2nd and 3rd order with saddle points, such as hyperbolic paraboloid z=x2-y2, and Monkey saddle z=x3-3xy2, are determined via implementation of gradient descent algorithm (its back propagation version) for 3 layers MPL. Demonstrated are conditions of generating function approximations with (1)highly irregular error distribution, (2)close to uniform error distribution as well as (3)enhanced approximation. In the last case the overall error is significantly smaller than that programmed in the algorithm to be attained via training patterns. Such enhanced solutions offer advantage of attaining highly accurate function representation within minimized resources of MLP (i.e. with minimized number of hidden neurons in the MLP).

关键词： cell broadband engine gradient descent algorithm multiple trajectories

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Extended Content-boosted Matrix Factorization algorithm for Recommender Systems

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Procedia Computer Science 2014年 35卷 417-426页

作者： Oleksandr Krasnoshchok Yngve Lamo Bergen University College Nygrdsgaten 112 Bergen 5020 Norway

关键词： Recommender System Matrix Factorization Content-Boosted Matrix Factorization Extended Content-Boosted Matrix Factorization gradient descent algorithm Content Aware Collaborative Filtering algorithm

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Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

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ARTIFICIAL INTELLIGENCE REVIEW 2013年第2期39卷 151-163页

作者： Senapati, M. R. Dash, P. K. Biju Patnaik Univ Technol Gandhi Engn Coll Dept Comp Sci & Engn Rourkela India SOA Univ Bhubaneswar Orissa India

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.

关键词： Local linear wavelet neural network Recursive least square gradient descent algorithm Wisconsin breast cancer (WBC)

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Robust adaptive fuzzy control of twin rotor MIMO system

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SOFT COMPUTING 2013年第10期17卷 1847-1860页

作者： Jahed, Maryam Farrokhi, Mohammad Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.

关键词： Adaptive fuzzy controller Twin rotor MIMO system gradient descent algorithm Lyapunov stability

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