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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Fei Wang Changshui Zhang H.C. Shen Jingdong Wang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering and Digital Technology Center Hong Kong University of Science and Technology Hong Kong China

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors’ labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Image reconstruction Computational geometry Information geometry Constraint optimization Image segmentation Semisupervised learning Laboratories intelligent systems automation Computer science

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Assessing features for electroencephalographic signal categorization

Assessing features for electroencephalographic signal catego...

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Sun, Shiliang Zhang, Changshui State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing 100084 China

ISBN: (纸本)0780388747

The classification of electroencephalographic (EEG) signals is an important issue in the ongoing research of brain-computer interface (BCI) technology. One such BCI uses slow cortical potential measures to infer user intent from the original brain activity. In the paper seven features based on the standard low-level signal properties are evaluated in their ability to classify brain activities, and thus make up for the scarcity of signal features for the current EEG signal categorization. In addition, a paradigm is proposed to select effective low-level features for EEG signal classification. Combining the features selected by the paradigm with the DC value of slow cortical potentials for categorization based on a Bayesian classifier, we obtained significant improvement on classification accuracy for data set Ia of BCI competition 2003, which is a typical representative of one kind of BCI data. © 2005 IEEE.

关键词： Electroencephalography

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A high performance face recognition system based on a huge face database

A high performance face recognition system based on a huge f...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Meng, Kai Su, Guang-Da Li, Cong-Cong Fu, Bo Zhou, Jun State Key Laboratory of Intelligent Technology and System Electronic Engineering Department Tsinghua University Beijing 100084 China

ISBN: (纸本)078039092X

This paper presents a high performance face recognition system, in which the face database has a large amount of 2.5 million faces. Huge as the face database is, the recognition processes in ordinary ways meets with great difficulties: the identification rate of most algorithms may decline significantly;meanwhile, querying on a large scale database may be quite time-consuming. In our system, a special distributed parallel architecture is proposed to speed up the computation. Furthermore, a multimodal part face recognition method based on principal component analysis (MMP-PCA) is adopted to perform the recognition task, and the MMX technology is introduced to accelerate the matching procedure. Practical results prove that this system has an excellent performance in recognition: when searching among 2,560,000 faces on 6 PC servers with Xeon 2.4GHz CPU, the querying time is only 1.094s and the identification rate is above 85% in most cases. Moreover, the greatest advantage of this system is not only increasing recognition speed but also breaking the upper limit of face data capacity. Consequently, the face data capability of this system can be extended to an arbitrarily large amount. © 2005 IEEE.

关键词： Face recognition

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Markov跳变线性奇异摄动系统鲁棒H∞控制

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自动化学报 2005年第5期31卷 779-781页

作者：刘华平孙富春李春文孙增圻 Department of Computer Science and Technology Tsinghua University Beijing 100084State Key Laboratory of Intelligent Technology and Systems Beijing 100084 Department of Automation Tsinghua University Beijing 100084 Department of Computer Science and Technology Tsinghua University Beijing 100084

In this paper, we study the robust control for uncertain Markov jump linear singularly perturbed systems (MJLSPS), whose transition probability matrix is unknown. An improved heuristic algorithm is proposed to solve the nonlinear matrix inequalities. The results of this paper can apply not only to standard, but also to nonstandard MJLSPS. Moreover, the proposed approach is independent of the perturbation parameter and therefore avoids the ill-conditioned numerical problems.

关键词： Markov过程跳变线性奇异系统鲁棒控制矩阵不等式

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INS/GPS Integrated system state Estimation Based on Hopfield Neural Network

INS/GPS Integrated System State Estimation Based on Hopfield...

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2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Hang Shi Jihong Zhu Zengqi Sun State Key Laboratory of Intelligent Technology and System Department of Computer Science and TechnologyTsinghua University

This paper aims at introducing state estimation in INS/GPS integrated system utilizing Hopfield neural ***/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings,including signal blockage for GPS and growth of position errors with time for *** of the present navigation systems rely on Kalman filtering methods to fuse *** Kalman filtering INS/GPS integration techniques have several inadequacies related to sensor error model,immunity to noise and *** method presented in this paper,obtains the optimal state estimation by minimizing the energy function of the Hopfield neural *** new estimator relaxes the assumptions made by the Kalman filter so that it is more *** results show that the new estimator performs similarly to the Kalman *** it has some advantages such as fast convergence,unbias and high precision during fusion process,despite of the inaccurate modeling errors,system disturbance,observation errors,and even the shortage of *** as the parallel computational mode and easily carried out in hardware of the Hopfield neural network,this fusion method can improve the navigation guidance accuracy,real time ability and practicability of the INS/GPS.

关键词： Hopfield INS/GPS Integrated system state Estimation Based on Hopfield Neural Network GPS

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New boosting methods of Gaussian processes for regression

New boosting methods of Gaussian processes for regression

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International Joint Conference on Neural Networks (IJCNN)

作者： Yangqiu Song Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

Feedforward neural networks are popular tools for nonlinear regression and classification problems. Gaussian Process (GP) can be viewed as an RBF neural network which have infinite number of hidden neurons. On regression problems, they can predict both the mean value and the variance of the given sample. Boosting is one of the most important recent developments in machine learning. Classification problems have dominated research on boosting to date. On the other hand, the application of boosting of regression has received less investigation. In this paper, we develop two boosting methods of GPs for regression according to the characteristic of them. We compare the performance of our ensembles with other boosting algorithms and find that our methods are more stable and essentially have less over-fitting problems than the other methods.

关键词： Boosting Gaussian processes Neural networks Feedforward neural networks Laboratories intelligent systems automation Feeds Neurons Machine learning

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Assessing features for electroencephalographic signal categorization

Assessing features for electroencephalographic signal catego...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shiliang Sun Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780388747

The classification of electroencephalographic (EEG) signals is an important issue in the ongoing research of brain-computer interface (BCI) technology. One such BCI uses slow cortical potential measures to infer user intent from the original brain activity. Seven features based on the standard low-level signal properties are evaluated for their ability to classify brain activities, and thus make up for the scarcity of signal features for the current EEG signal categorization. In addition, a paradigm is proposed to select effective low-level features for EEG signal classification. Combining the features selected by the paradigm with the DC value of slow cortical potentials for categorization based on a Bayesian classifier, we obtained significant improvement on classification accuracy for data set Ia of the BCI competition 2003, which is a typical representative of one kind of BCI data.

关键词： Electroencephalography Brain Laboratories Pattern classification Communication system control Muscles Rhythm Speech processing Biomedical signal processing Sun

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A fixed-point ICA algorithm with initialization constraint

A fixed-point ICA algorithm with initialization constraint

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International Conference on Communications, Circuits and systems (ICCCAS)

作者： Bin Wang Wenkai Lu State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780390156

We propose a novel approach, the fixed-point algorithm (FastICA) with initialization constraint, for performing independent component analysis (ICA). In order to achieve a reliable convergence during estimating the blind source components, both the third- and fourth-order statistics are taken into account when diagonalizing the cumulant tensors. By combining these high-order statistics, an initialization constraint is introduced into the decomposition procedure of the independent components by FastICA. The experimental results demonstrate that the improved algorithm can achieve a better performance than the original FastICA without increasing the computation cost dramatically. The simulations involving source signals with different distributions show that our algorithm can adapt most source signals, including some complicated and asymmetric distributions.

关键词： Independent component analysis Signal processing algorithms Multidimensional signal processing Speech Statistical distributions Computational efficiency Statistical analysis Signal analysis Laboratories intelligent systems

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A new algorithm for pattern optimization in protein-protein interaction extraction system

A new algorithm for pattern optimization in protein-protein ...

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Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005

作者： Hao, Yu Zhu, Xiaoyan Li, Ming State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science University of Waterloo Waterloo Ont. N2L 3G1 Canada

In pattern matching based Protein-Protein Interaction Extraction systems, patterns generated manually or automatically exist erroneous and redundancy, which greatly affect the system's performance. In this paper, a MDL-based pattern optimizing algorithm is proposed to filter out the bad patterns and redundancy. Experiments show that our algorithm is effective in improving the system's performance while greatly cutting down the number of patterns. It also has excellent generalizability which is important in implementing practical systems. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Pattern recognition

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JNS/GPS Integrated system state Estimation Based on Hopfield Neural Network

JNS/GPS Integrated System State Estimation Based on Hopfield...

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International Conference on Neural Networks and Brain (ICNNB)

作者： Hang Shi Jihong Zhu Zengqi Sun State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing China

This paper aims at introducing state estimation in INS/GPS integrated system utilizing Hopfield neural network. INS/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and growth of position errors with time for INS. Most of the present navigation systems rely on Kalman filtering methods to fuse data. Present Kalman filtering INS/GPS integration techniques have several inadequacies related to sensor error model, immunity to noise and observability. The method presented in this paper, obtains the optimal state estimation by minimizing the energy function of the Hopfield neural network. This new estimator relaxes the assumptions made by the Kalman filter so that it is more versatile. Simulation results show that the new estimator performs similarly to the Kalman filter. Furthermore it has some advantages such as fast convergence, unbias and high precision during fusion process, despite of the inaccurate modeling errors, system disturbance, observation errors, and even the shortage of observation. Also as the parallel computational mode and easily carried out in hardware of the Hopfield neural network, this fusion method can improve the navigation guidance accuracy, real time ability and practicability of the INS/GPS

关键词： Global Positioning system state estimation Hopfield neural networks Navigation Kalman filters Filtering Fuses Observability Computational modeling Convergence

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