The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Yuan Yuan - One of the best experts on this subject based on the ideXlab platform.
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Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology, 2010Co-Authors: Yanwei Pang, Yuan YuanAbstract:Tensor Analysis plays an important role in modern image and vision computing problems. Most of the existing Tensor Analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based Tensor Analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.
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Robust Tensor Analysis with L1-Norm Yanwei Pang, Senior Member, IEEE, Xuelong Li, Senior Member, IEEE and Yuan Yuan Senior Member, IEEE
2010Co-Authors: Yanwei Pang, Yuan YuanAbstract:Tensor Analysis plays an important role in modern image and vision computing problems. Most of the existing Tensor Analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based Tensor Analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach. Index Terms—L1-norm, outlier, Tensor Analysis.
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ICIP - Object trajectory clustering via Tensor Analysis
2009 16th IEEE International Conference on Image Processing (ICIP), 2009Co-Authors: Huiyu Zhou, Dacheng Tao, Yuan YuanAbstract:In this paper we present a new video object trajectory clustering algorithm1, which allows us to model and analyse the patterns of object behaviors based on the extracted features using Tensor Analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by Tensor Analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.
Spiros Papadimitriou - One of the best experts on this subject based on the ideXlab platform.
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window based Tensor Analysis on high dimensional and multi aspect streams
International Conference on Data Mining, 2006Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent- window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
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ICDM - Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
Sixth International Conference on Data Mining (ICDM'06), 2006Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent- window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
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Tensor Analysis on Multi-aspect Streams
Learning from Data Streams, 1Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Aside from time stamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent-window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real data sets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
Jimeng Sun - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Bayesian Tensor Analysis
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Dacheng Tao, Jimeng Sun, Jialie Shen, Stephen J. Maybank, Christos FaloutsosAbstract:Vector data are normally used for probabilistic graphical models with Bayesian inference. However, Tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and Tensors in conventional statistical tasks, e.g., automatic model selection, this paper proposes a decoupled probabilistic algorithm, named Bayesian Tensor Analysis (BTA). BTA automatically selects a suitable model for Tensor data, as demonstrated by empirical studies.
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window based Tensor Analysis on high dimensional and multi aspect streams
International Conference on Data Mining, 2006Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent- window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
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ICDM - Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
Sixth International Conference on Data Mining (ICDM'06), 2006Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent- window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
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KDD - Beyond streams and graphs: dynamic Tensor Analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06, 2006Co-Authors: Jimeng Sun, Dacheng Tao, Christos FaloutsosAbstract:How do we find patterns in author-keyword associations, evolving over time? Or in Data Cubes, with product-branch-customer sales information? Matrix decompositions, like principal component Analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example.We propose to envision such higher order data as Tensors,and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic Tensor Analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming Tensor Analysis method, which provides a fast, streaming approximation to DTA.We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets.
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Tensor Analysis on Multi-aspect Streams
Learning from Data Streams, 1Co-Authors: Jimeng Sun, Spiros PapadimitriouAbstract:Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Aside from time stamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, Tensor streams, and an effective algorithmic framework, window-based Tensor Analysis (WTA). Two variations of WTA, independent-window Tensor Analysis (IW) and moving-window Tensor Analysis (MW), are presented and evaluated extensively on real data sets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
Yanwei Pang - One of the best experts on this subject based on the ideXlab platform.
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Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology, 2010Co-Authors: Yanwei Pang, Yuan YuanAbstract:Tensor Analysis plays an important role in modern image and vision computing problems. Most of the existing Tensor Analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based Tensor Analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.
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Robust Tensor Analysis with L1-Norm Yanwei Pang, Senior Member, IEEE, Xuelong Li, Senior Member, IEEE and Yuan Yuan Senior Member, IEEE
2010Co-Authors: Yanwei Pang, Yuan YuanAbstract:Tensor Analysis plays an important role in modern image and vision computing problems. Most of the existing Tensor Analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based Tensor Analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach. Index Terms—L1-norm, outlier, Tensor Analysis.
Malcolm H. Levitt - One of the best experts on this subject based on the ideXlab platform.
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Spherical Tensor Analysis of nuclear magnetic resonance signals
The Journal of Chemical Physics, 2005Co-Authors: Jacco D. Van Beek, Marina Carravetta, Gian Carlo Antonioli, Malcolm H. LevittAbstract:In a nuclear magnetic-resonance (NMR) experiment, the spin density operator may be regarded as a superposition of irreducible spherical Tensor operators. Each of these spin operators evolves during the NMR experiment and may give rise to an NMR signal at a later time. The NMR signal at the end of a pulse sequence may, therefore, be regarded as a superposition of spherical components, each derived from a different spherical Tensor operator. We describe an experimental method, called spherical Tensor Analysis (STA), which allows the complete resolution of the NMR signal into its individual spherical components. The method is demonstrated on a powder of a C13-labeled amino acid, exposed to a pulse sequence generating a double-quantum effective Hamiltonian. The propagation of spin order through the space of spherical Tensor operators is revealed by the STA procedure, both in static and rotating solids. Possible applications of STA to the NMR of liquids, liquid crystals, and solids are discussed.