Browsing 08 Information and Computing Sciences by Author "Li, Jun"

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Browsing 08 Information and Computing Sciences by Author "Li, Jun"

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  • Li, Jun; Tao, Dacheng (AAAI, 2013)
  • Li, Jun; Tao, Dacheng (IEEE-Inst Electrical Electronics Engineers Inc, 2013)
    Factorization-based techniques explain arrays of observations using a relatively small number of factors and provide an essential arsenal for multi-dimensional data analysis. Most factorization models are, however, developed ...
  • Li, Jun; Zhang, Peng; Tan, Jianlong; Liu, Ping; Guo, Li (ACM, 2011)
    Cloud computing represents one of the most important research directions for modern computing systems. Existing research efforts on Cloud computing were all focused on designing advanced storage and query techniques for ...
  • Li, Jun; Zhang, Peng; Cao, Yanan; Liu, Ping; Guo, Li (IEEE, 2012)
    Behavior targeting (BT) is a promising tool for online advertising. The state-of-the-art BT methods, which are mainly based on regression models, have two limitations. First, learning regression models for behavior targeting ...
  • Zhang, Peng; Li, Jun; Wang, Peng; Gao, Byron; Zhu, Xingquan; Guo, Li (ACM, 2011)
    Ensemble learning has become a common tool for data stream classification, being able to handle large volumes of stream data and concept drifting. Previous studies focus on building accurate prediction models from stream ...
  • Li, Jun; Tao, Dacheng (IEEE-Inst Electrical Electronics Engineers Inc, 2013)
    Expressing data as linear functions of a small number of unknown variables is a useful approach employed by several classical data analysis methods, e.g., factor analysis, principal component analysis, or latent semantic ...
  • Li, Jun; Bian, Wei; Tao, Dacheng; Zhang, Chengqi (Springer-Verlag Berlin / Heidelberg, 2011)
    The capability of inferring colours from the texture (grayscale contents) of an image is useful in many application areas, when the imaging device/environment is limited. Traditional colour assignment involves intensive ...
  • Li, Jun; Tao, Dacheng (IEEE-Inst Electrical Electronics Engineers Inc, 2012)
    Principal component analysis (PCA) computes a succinct data representation by converting the data to a few new variables while retaining maximum variation. However, the new variables are dif?cult to interpret, because ...
  • Li, Jun; Tao, Dacheng; Li, Xuelong (Elsevier, 2012)
    For image analysis, an important extension to principal component analysis (PCA) is to treat an image as multiple samples, which helps alleviate the small sample size problem. Various schemes of transforming an image to ...
  • Zhang, Zhang; Cheng, Jun; Li, Jun; Bian, Wei; Tao, Dacheng (Oxford Press, 2012)
    In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, ...
  • Li, Jun; Tao, Dacheng (IEEE-inst Electrical Electronics Engineers Inc, 2013)
    Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this paper, we address the problem of determining the intrinsic dimensionality of a general type data population by selecting the ...
  • Li, Jun; Tao, Dacheng (IEEE- Computer Society, 2011)
    This paper addresses the problem of learning based single image super-resolution. Previous research on this problem employs human user to provide a set of images that are similar to the target image as a reference. Then ...