Browse Research and Tech Reports - Computer Science by Author "Cai, Deng"

  • Cai, Deng; He, Xiaofei; Han, Jiawei (2007-08)
    Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2008-02)
    Recently Non-negative Matrix Factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-07)
    Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing, including data mining, information retrieval, and pattern recognition. We consider the case where ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-04)
    Most of the existing learning algorithms take vectors as their input data. A function is then learned in such a vector space for classification, clustering, or dimensionality reduction. However, in some situations, there ...

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  • Cai, Deng; Shao, Zheng; He, Xiaofei; Yan, Xifeng; Han, Jiawei (2005-03)
    Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only ...

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  • Cai, Deng; Mei, Qiaozhu; He, Xiaofei; Han, Jiawei (2008-01)
    Topic modeling has been a key problem for document analysis. One of the canonical approaches for topic modeling is Probabilistic Latent Semantic Indexing, which maximizes the joint probability of documents and terms in the ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-07)
    A novel approach to linear dimensionality reduction is introduced that is based on Locality Preserving Projections (LPP) with a discretized Laplacian smoothing term. The choice of penalty allows us to incorporate prior ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-07)
    Graph-based approaches for semi-supervised learning have received increasing amount of interest in recent years. Despite their good performance, many pure graph based algorithms do not have explicit functions and can not ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2007-05)
    Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) matrix ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2007-05)
    Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection functions of LDA are commonly obtained by maximizing the between class covariance and ...

    application/pdf

    application/pdfPDF (249Kb)
  • Cai, Deng; He, Xiaofei; Han, Jiawei (2005-05)
    Linear dimensionality reduction techniques have been widely used in pattern recognition and computer vision, such as face recognition, image retrieval, etc. The typical methods include Principal Component Analysis (PCA) ...

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  • Cai, Deng; He, Xiaofei; Wen, Ji-Rong; Han, Jiawei; Ma, Wei-Ying (2006-04)
    We consider the problem of text representation and categorization. Conventionally, a text document is represented by a vector in high dimensional space. Some learning algorithms are then applied in such a vector space for ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-04)
    Vector Space Model (VSM) has been at the core of information retrieval for the past decades. VSM considers the documents as vectors in high dimensional space. In such a vector space, techniques like Latent Semantic Indexing ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2005-09)
    Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modelled by low dimensional linear spaces. The typical methods for learning a ...

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