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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1778 条记录，以下是1591-1600 订阅

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Sub-pattern non-negative matrix factorization based on random subspace for face recognition

Sub-pattern non-negative matrix factorization based on rando...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Zhu, Yu-Lian Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn Nanjing 210016 Peoples R China

ISBN: (纸本)9781424410651

Non-negative matrix factorization (NMF) as a part-based representation method allows only additive combinations of non-negative basis components to represent the original data, so it provides a realistic approximation to the original data. However, NMF does not work well when directly applied to face recognition due to its global linear decomposition;this intuitively results in a degradation of recognition performance and non-robustness to the variation in illumination, expression and occlusion. In this paper, we propose a robust method, random subspace sub-pattern NMF (RS-SpNMF), especially for face recognition. Unlike the traditional random subspace method (RSM), which completely randomly selects the features from the whole original pattern feature set, the proposed method randomly samples,features from each local region (or a sub-image) partitioned from the original face image and performs NMF decomposition on each sampled feature set. More specially, we first divide a face image into several sub-images in a deterministic way, then construct a component classifier on sampled feature subset from each sub-image set, and finally combine all of component classifiers for the final decision. Experiments on three benchmarks face databases (ORL,Yale and AR) show that the proposed method is effective, especially to the occlusive face image.

关键词： NMF face recognition PCA LNMF SpPCA SpNMF RS-SpNMF

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Outlier detection with kernel density functions 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Latecki, Longin Jan Lazarevic, Aleksandar Pokrajac, Dragojub Temple Univ CIS Dept Philadelphia PA 19122 USA United Technol Res Ctr E Hartford CT 06108 USA Delware State Univ CIS Dept CREOSA AMRC Dover DE 19901 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).

关键词： Financial data processing

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A new incremental optimal feature extraction method for on-line applications

引用

4th international conference on Image Analysis and recognition

作者： Ghassabeh, Youness Aliyari Moghaddam, Hamid Abrishami KN Toosi Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9783540742586

In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adaptive algorithms for the computation of the square root of the inverse covariance matrix Sigma (-1/2) are introduced. the proof for the convergence of the new adaptive algorithm is given by presenting the related cost function and discussing about its initial conditions. the new adaptive algorithms are used before an adaptive principal component analysis algorithm in order to construct an adaptive multivariate multi-class LDA algorithm. Adaptive nature of the new optimal feature extraction method makes it appropriate for on-line pattern recognition applications. Both adaptive algorithms in the proposed structure are trained simultaneously, using a stream of input data. Experimental results using synthetic and real multi-class multi-dimensional sequence of data, demonstrated the effectiveness of the new adaptive feature extraction algorithm.

关键词： adaptive learning algorithm adaptive linear discriminant analysis feature extraction

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True 2-D all phase filter bank and its application for image compression

True 2-D all phase filter bank and its application for image...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Xu, Ni-Ni Wang, Li-Ning Xiao, Zhi-Tao Tianjin Polytech Univ Sch Commun & Elect Informat Engn Tianjin 300160 Peoples R China

ISBN: (纸本)9781424410651

Based on all phase theory this paper designed three kinds of true 2-D all phase filter bank (true 2-D APFB), which can be used to decompose and recompose image data in true 2-D directly. If quantification error of filters is ignored, the true 2-D APFBs have perfect reconstruction property. To reduce computation, they are implemented in lifting scheme. Simulation has shown that true 2-D APFBs have nicer data compression property. With the same compression rate, PSNR of IDCT_AFB7.7 is less 0.7dB at most than Daubechies9/7 wavelet's, for true 2-D APFBs adopt quadtree SPIHT coding method which is suitable for separable 2-D wavelet transform. For true 2-D filter banks, binary tree SPIHT coding should be adopted to gel better performance in compression.

关键词： true 2-D all phase filter bank lifting scheme data compression

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Categorizing evolved CoreWar warriors using EM and attribute evaluation 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Pracner, Doni Tomesev, Nenad Radovanovic, Milos Ivanovic, Mirjana Univ Novi Sad Fac Sci Dept Math & Informat Trg D Obradov 4 Novi Sad 21000 Serbia

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

CoreWar is a computer simulation where two programs written in an assembly language called redcode compete in a virtual memory array. these programs are referred to as warriors. Over more than twenty years of development a number of different battle strategies have emerged, making it possible to identify different warrior types. Systems for automatic warrior creation appeared more recently, evolvers being the dominant kind. this paper describes an attempt to analyze the output of the CCAI evolver, and explores the possibilities for performing automatic categorization by warrior type using representations based on redcode source, as opposed to instruction execution frequency. Analysis was performed using EM clustering, as well as information gain and gain ratio attribute evaluators, and revealed which mainly brute-force types of warriors were being generated. this, along with the observed correlation between clustering and the workings of the evolutionary algorithm justifies our approach and calls for more extensive experiments based on annotated warrior benchmark collections.

关键词： Computer simulation

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Mining frequent trajectories of moving objects for location prediction 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Morzy, Mikolaj Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Advances in wireless and mobile technology flood us with amounts of moving object data that preclude all means of manual data processing. the volume of data gathered from position sensors of mobile phones, PDAs, or vehicles, defies human ability to analyze the stream of input data. On the other hand, vast amounts of gathered data hide interesting and valuable knowledge patterns describing the behavior of moving objects. thus, new algorithms for mining moving object data are required to unearth this knowledge. An important function of the mobile objects management system is the prediction of the unknown location of an object. In this paper we introduce a data mining approach to the problem of predicting the location of a moving object. We mine the database of moving object locations to discover frequent trajectories and movement rules. then, we match the trajectory of a moving object with the database of movement rules to build a probabilistic model of object location. Experimental evaluation of the proposal reveals prediction accuracy close to 80%. Our original contribution includes the elaboration on the location prediction model, the design of an efficient mining algorithm, introduction of movement rule matching strategies, and a thorough experimental evaluation of the proposed model.

关键词： data mining

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Generic probability density function reconstruction for randomization in privacy-preserving data mining 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Tan, Vincent Yan Fu Ng, See-Kiong MIT 77 Massachusetts Ave Cambridge MA 02139 USA Inst Infocomm Res I2R Singapore 119613 Singapore

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

data perturbation with random noise signals has been shown to be useful for data hiding in privacy-preserving data mining. Perturbation methods based on additive randomization allows accurate estimation of the Probability Density Function (PDF) via the Expectation-Maximization (EM) algorithm but it has been shown that noise-filtering techniques can be used to reconstruct the original data in many cases, leading to security breaches. In this paper, we propose a generic PDF reconstruction algorithm that can be used on non-additive (and additive) randomization techiques for the purpose of privacy-preserving data mining. this two-step reconstruction algorithm is based on Parzen-Window reconstruction and Quadratic Programming over a convex set - the probability simplex. Our algorithm eliminates the usual need for the iterative EM algorithm and it is generic for most randomization models. the simplicity of our two-step reconstruction algorithm, without iteration, also makes it attractive for use when dealing with large datasets.

关键词： randomization privacy-preserving data mining Parzen-windows quadratic programming convex set

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Transductive learning from relational data 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Ceci, Michelangelo Appice, Annalisa Barile, Nicola Malerba, Donato Univ Bari Dipartimento Informat Via Orabona 4 I-70126 Bari Italy

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Transduction is an inference mechanism "from particular to particular". Its application to classification tasks implies the use of both labeled (training) data and unlabeled (working) data to build a classifier whose main goal is that of classifying (only) unlabeled data as accurately as possible. Unlike the classical inductive setting, no general rule valid for all possible instances is generated. Transductive learning is most suited for those applications where the examples for which a prediction is needed are already known when training the classifier. Several approaches have been proposed in the literature on building transductive classifiers from data stored in a single table of a relational database. Nonetheless, no attention has been paid to the application of the transduction principle in a (multi-) relational setting, where data are stored in multiple tables of a relational database. In this paper we propose a new transductive classifier, named TRANSC, which is based on a probabilistic approach to making transductive inferences from relational data. this new method works in a transductive setting and employs a principled probabilistic classification in multi-relational data mining to face the challenges posed by some spatial data mining problems. Probabilistic inference allows us to compute the class probability and return, in addition to result of transductive classification, the confidence in the classification. the predictive accuracy of TRANSC has been compared to that of its inductive counterpart in an empirical study involving both a benchmark relational dataset and two spatial datasets. the results obtained are generally in favor of TRANSC, although improvements are small by a narrow margin.

关键词： Inference engines

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Tracking of multiple targets using on-line learning for appearance model adaptation

引用

4th international conference on Image Analysis and recognition

作者： Pernkopf, Franz Graz Univ Technol A-8010 Graz Austria

ISBN: (纸本)9783540742586

We propose visual tracking of multiple objects (faces of people) in a meeting scenario based on low-level features such as skin-color, target motion, and target size. Based on these features automatic initialization and termination of objects is performed. Furthermore, on-line learning is used to incrementally update the models of the tracked objects to reflect the appearance changes. For tracking a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach.

关键词： Target tracking

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Research on video segmentation via active learning

Research on video segmentation via active learning

引用

4th international conference on Image and Graphics

作者： Tan, Wenwei Teng, Shaohua Zhang, Wei Guangdong Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9780769529295

We propose a method to video segmentation via active learning. Shot segmentation is an essential first step to video segmentation.. the color histogram-based shot boundary detection algorithm is one of the most reliable variants of histogram-based detection algorithms. It is not unreasonable to assume that the color content does not change rapidly within but across shots. thus, we present a metric based on blocked color histogram (BCH) for inter-frame difference. Our metric is the normalized intersection of BCH between contiguous frames. Hard cuts and gradual shot transitions can be detected as valleys in the time series of the differences between color histograms of contiguous frames or of frames a certain distance apart. We try to estimate the valleys on the frame-to-frame difference curve. Each kind of shot transition (Cut or Gradual shot transition) has its own characteristic. pattern corresponding with valleys. therefore shot detection can be viewed as pattern recognition. We employ the support vector machine (SVM) via active learning to classify shot boundaries and non-boundaries Our method is evaluated on the TRECVID benchmarking platform and the experimental results reveal the effectiveness and robustness of the method.

关键词： Support vector machines

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