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Two Dimensional Nonnegative Matrix Factorization

Two Dimensional Nonnegative Matrix Factorization

引用

IEEE International Conference on Image Processing

作者： Quanquan Gu Jie Zhou Department of Automation Tsinghua University Beijing China State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

Nonnegative Matrix Factorization (NMF) has been widely used in computer vision and pattern recognition. It aims to find two nonnegative matrices whose product can well approximate the original matrix, which naturally leads to parts-based representation. In this paper, we propose a Two Dimensional Nonnegative Matrix Factorization (2DNMF), specifically for a sequence of matrices. In contrast to NMF which applies for only a single matrix, and finds only one base matrix, 2DNMF aims to find two base matrices to represent the input matrices in a low dimensional matrix subspace. It not only inherits the advantages of NMF, but also owns the properties low computational complexity, as well as high recognition accuracy. Experiments on benchmark image recognition data sets illustrate that the proposed method is very effective and efficient.

关键词： Sparse matrices Image recognition Principal component analysis Laboratories Computer vision Pattern recognition Computational complexity intelligent systems Information science Automation

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Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

Learning the Shared Subspace for Multi-task Clustering and T...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

There are many clustering tasks which are closely related in the real world, e.g. clustering the Web pages of different universities. However, existing clustering approaches neglect the underlying relation and treat these clustering tasks either individually or simply together. In this paper, we will study a novel clustering paradigm, namely multi-task clustering, which performs multiple related clustering tasks together and utilizes the relation of these tasks to enhance the clustering performance. We aim to learn a subspace shared by all the tasks, through which the knowledge of the tasks can be transferred to each other. The objective of our approach consists of two parts: (1) Within-task clustering: clustering the data of each task in its input space individually; and (2) Cross-task clustering: simultaneous learning the shared subspace and clustering the data of all the tasks together. We will show that it can be solved by alternating minimization, and its convergence is theoretically guaranteed. Furthermore, we will show that given the labels of one task, our multi-task clustering method can be extended to transductive transfer classification (a.k.a. cross-domain classification, domain adaption). Experiments on several cross-domain text data sets demonstrate that the proposed multi-task clustering outperforms traditional single-task clustering methods greatly. And the transductive transfer classification method is comparable to or even better than several existing transductive transfer classification approaches.

关键词： Web pages Clustering methods Machine learning Laboratories Data mining intelligent systems Information science Automation Convergence History

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Multiple Kernel Maximum Margin Criterion

Multiple Kernel Maximum Margin Criterion

引用

IEEE International Conference on Image Processing

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList Department of Automation Tsinghua University Beijing China

Maximum Margin Criterion (MMC) is an efficient and robust feature extraction method, which has been proposed recently. Like other kernel methods, when MMC is extended to Reproducing Kernel Hilbert Space via kernel trick, its performance heavily depends on the choice of kernel. In this paper, we address the problem of learning the optimal kernel over a convex set of prescribed kernels for Kernel MMC (KMMC). We will give an equivalent graph based formulation of MMC, based on which we present Multiple Kernel Maximum Margin Criterion (MKMMC). Then we will show that MKMMC can be solved via alternative optimization schema. Experiments on benchmark image recognition data sets show that the proposed method outperforms KMMC via cross validation, as well as some state of the art methods.

关键词： Kernel Feature extraction Covariance matrix Hilbert space Principal component analysis Scattering Laboratories Space technology Image recognition intelligent systems

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Regular simplex criterion: A novel feature extraction criterion

Regular simplex criterion: A novel feature extraction criter...

引用

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

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList) Department of Automation Tsinghua University Beijing China

Feature extraction is an important topic in machine learning. There are two representative criterions for feature extraction, i.e. Fisher Criterion and Maximum Margin Criterion. In this paper, we propose a new criterion, called Regular Simplex Criterion. This criterion requires that samples from the same class are projected to the same point, while samples from different classes have unit distance. Under this criterion, we present a novel dimensionality reduction method, namely Linear Simplex Analysis (LSA). LSA is solved by multivariate linear regression with a specific definition of class indicator matrix which has a strong geometrical interpretation, i.e. each column of this matrix corresponds to a vertex of a regular simplex. Several variants of LSA, e.g. Regularized Simplex Analysis (RSA) and Kernel Simplex Analysis (KSA), are also proposed. Encouraging experimental results on UCI machine learning database indicate that the new criterion as well as the proposed methods are very effective.

关键词： Feature extraction Machine learning Kernel Linear discriminant analysis Laboratories Linear regression Spatial databases Principal component analysis intelligent systems Learning systems

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A Novel Sequence Based Localization Approach for Wireless Sensor Networks

A Novel Sequence Based Localization Approach for Wireless Se...

引用

International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Xiao Xu Zhidong Deng Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

In wireless sensor networks, two types of emerging range-free and high-accuracy localization approaches including the approximate-point-in-triangulation test (APIT) and the sequence based localization (SBL) greatly depend on density of anchor nodes and connectivity of network. In this paper, we present a novel localization approach that combines APIT with SBL, called SB-APIT. First, we use APIT approaches to determine boundary. We then sequentially measure the received signal strength indicator (RSSI) value of target node within the range of one-hop neighbor nodes and rank the RSSI values to yield a location sequence. Furthermore, the coordinates of the area's geometric center is calculated as the target node's coordinates. Finally, the simulation results obtained with 200 nodes show that our new approach reduces the average localization error by about 5%. It considerably improves the accuracy of localization.

关键词： Wireless sensor networks Testing intelligent sensors Intrusion detection Computer science Sensor systems Laboratories intelligent systems intelligent networks Radio communication

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Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model

Beyond Banditron: A Conservative and Efficient Reduction for...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Guangyun Chen Gang Chen Jianwen Zhang Shuo Chen Changshui Zhang State Key Laboratory of Intelligent Technologies and Systems Tsinghua National Laboratory of Information Science and Technology TNList Department of Automation Tsinghua University Beijing China

In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. ¿true¿ when the predicting label is right or ¿false¿ when the predicting label is wrong, this new kind of problems arouses much of researchers' interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.

关键词： Predictive models Humans Clustering algorithms Partitioning algorithms Automobiles Personnel Data mining Vocabulary Training data Costs

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Task Scheduling for Heterogeneous Computing Based on Bayesian Optimization Algorithm

Task Scheduling for Heterogeneous Computing Based on Bayesia...

引用

International Conference on Computational Intelligence and Security

作者： Jiadong Yang Hua Xu Peifa Jia State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781424454112

Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platform, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on Bayesian optimization algorithm (BOA) for heterogeneous computing environment. In the proposed algorithm, BOA constructs and updates Bayesian network according to the task graph of scheduling problems to find the optimal solution assigning tasks to different processors, and the execution sequence of tasks on the same processor is set by the heuristic used in the list scheduling approach. The proposed algorithm is sufficiently evaluated and compared with the related approaches by means of the empirical studies on benchmark applications. The experimental results confirm that the proposed algorithm is able to deliver more efficient schedules. Further experiments also indicate that the proposed algorithm maintains almost the same performance with different parameter settings.

关键词： Processor scheduling Bayesian methods Scheduling algorithm Computational intelligence Laboratories Clustering algorithms Computer security Information security National security High performance computing

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Vehicle tracking based on co-learning particle filter

Vehicle tracking based on co-learning particle filter

引用

2009 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Weilong Ye Huaping Liu Fuchun Sun Meng Gao Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems TNLIST Tsinghua University Beijing China Shijiazhuang Railway Institute Hefei China

In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches belong to ¿self-learning¿ fashion and easily tend to drift. The major difference between the proposed approach and previous works is that the SVM detector and the likelihood function can be mutually updated in a co-learning manner. By adopting the co-learning technology, the unlabelled samples which are generated during tracking are utilized to progressively modify the SVM detector and update the observation likelihood; therefore the resulting tracker is more robust and effectively avoids the drift problem. Finally, the performance of the proposed approach is evaluated using extensive real visual tracking examples.

关键词： Particle tracking Particle filters Radar tracking Detectors Semisupervised learning intelligent vehicles Support vector machines intelligent robots Sun Proposals

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Learning a music similarity measure on automatic annotations with application to playlist generation

Learning a music similarity measure on automatic annotations...

引用

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

作者： Linxing Xiao Lie Lu Frank Seide Jie Zhou State Key Laboratory on Intelligent Technology and Systems Department of Automation Tsinghua National Laboratory of Information Science and Technology Tsinghua University Beijing China Microsoft Research Asia Beijing China

This paper presents an approach to learn a better music similarity measure and presents an application to music playlist generation. Different from previous work, in our approach, automatically detected music attributes are used to represent each song. A set of kernels is employed in similarity measure, with each kernel measuring on a subset of music attributes and having a different importance weight. In automatic music playlist generation, a ranking method is presented, which considers multiple seed songs and possible outlier seed. Experiments show the effectiveness of the proposed approach, and the quality of the playlist generated based on automatic annotations is comparable to that based on manual annotations.

关键词： Kernel Asia Laboratories Recommender systems Music information retrieval Humans Multiple signal classification intelligent systems Information science Automation

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Specialized Review Selection for Feature Rating Estimation

Specialized Review Selection for Feature Rating Estimation

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Chong Long Jie Zhang Minlie Huang Xiaoyan Zhu Ming Li Bin Ma State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University China School of Computer Science University of Waterloo Canada

On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate feature ratings of the products. This approach selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features. Experiments on real data show that overall ratings of the specialized reviews can be used to represent their feature ratings. The average of these overall ratings can be used by recommender systems to provide feature specific recommendations that better help users make purchasing decisions.

关键词： intelligent agent Recommender systems Computer science Conferences state estimation intelligent systems Information science Data mining Text mining Costs

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