Now showing items 27-36 of 36

    • Relationships Between Support Vector Classifiers and Generalized Linear Discriminant Analysis on Support Vectors 

      Kim, Hyunsoo; Drake, Barry L.; Park, Haesun (Georgia Institute of Technology, 2006)
      The linear discriminant analysis based on the generalized singular value decomposition (LDA/GSVD) has been introduced to circumvent the nonsingularity restriction inherent in the classical LDA. The LDA/GSVD provides a ...
    • A Roofline Model of Energy 

      Choi, Jee Whan; Vuduc, Richard W. (Georgia Institute of Technology, 2012)
      We describe an energy-based analogue of the time-based roofline model of Williams, Waterman, and Patterson (Comm. ACM, 2009). Our goal is to explain—in simple, analytic terms accessible to algorithm designers and ...
    • Single-tree GMM training 

      Curtin, Ryan R. (Georgia Institute of Technology, 2015-05-27)
    • SNARE: Spatio-temporal Network-level Automatic Reputation Engine 

      Feamster, Nick; Gray, Alexander G.; Krasser, Sven; Syed, Nadeem Ahmed (Georgia Institute of Technology, 2008)
      Current spam filtering techniques classify email based on content and IP reputation blacklists or whitelists. Unfortunately, spammers can alter spam content to evade content based filters, and spammers continually change ...
    • Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares 

      Kim, Hyunsoo; Park, Haesun (Georgia Institute of Technology, 2006)
      Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Non-negative matrix factorization (NMF) ...
    • Sparse Nonnegative Matrix Factorization for Clustering 

      Kim, Jingu; Park, Haesun (Georgia Institute of Technology, 2008)
      Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating its formulation to other methods such as K-means clustering. We show how interpreting the objective function of K-means ...
    • To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation 

      Choo, Jaegul; Lee, Daniel; Dilkina, Bistra; Zha, Hongyuan; Park, Haesun (Georgia Institute of Technology, 2013)
      Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. ...
    • Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons 

      Kim, Jingu; Park, Haesun (Georgia Institute of Technology, 2008)
      Nonnegative Matrix Factorization (NMF) is a dimension reduction method that has been widely used for various tasks including text mining, pattern analysis, clustering, and cancer class discovery. The mathematical formulation ...
    • VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data 

      Choo, Jaegul; Lee, Changhyun; Clarkson, Edward; Liu, Zhicheng; Lee, Hanseung; Chau, Duen Horng (Polo); Li, Fuxin; Kannan, Ramakrishnan; Stolper, Charles D.; Inouye, David; Mehta, Nishant; Ouyang, Hua; Som, Subhojit; Gray, Alexander; Stasko, John; Park, Haesun (Georgia Institute of Technology, 2013)
      We present a visual analytics system called VisIRR, which is an interactive visual information retrieval and recommendation system for document discovery. VisIRR effectively combines both paradigms of passive pull through ...
    • Visualize It-Wise! An Iteration-Wise Computational Framework for Real-Time Visual Analytics 

      Choo, Jaegul; Lee, Changhyun; Park, Haesun (Georgia Institute of Technology, 2013)
      Abstract Visual analytics has been gaining increasing interest due to its fascinating characteristic that leverages both humans’ visual perception and the power of computing. Although various computational methods are ...