Person:
Park, Haesun

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Publication Search Results

Now showing 1 - 10 of 19
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    PIVE: A Per-Iteration Visualization Environment for Supporting Real-time Interactions with Computational Methods
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Changhyun ; Park, Haesun
    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 being proposed, they do not properly support visual analytics. One of the biggest obstacles towards their real-time visual analytic integration is their high computational complexity. As a way to tackle this problem, this paper presents PIVE, a Per-Iteration Visualization Environment for supporting real-time interactive visualization with computational methods. The main idea behind PIVE is that most advanced computational methods work by refining the solution iteratively. By visually delivering the result from each iteration to users, the proposed framework enables users to quickly acquire the information that the computational method provides as well as the ability to perform continuous interactions with them in real time. We show the effectiveness of PIVE in terms of real-time visualization and interaction capabilities by customizing various dimension reduction methods such as principal component analysis, multidimensional scaling, and t-distributed stochastic neighborhood embedding, and clustering method s such as k-means and latent Dirichlet allocation.
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    Visualize It-Wise! An Iteration-Wise Computational Framework for Real-Time Visual Analytics
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Changhyun ; Park, Haesun
    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 being proposed, they do not properly support visual analytics. One of the biggest obstacles towards their real-time visual analytic integration is their high computational complexity. As a way to tackle this problem, this paper presents an iteration-wise computational framework, motivated by the fact that most advanced computational methods work by refining the solution iteratively. By visually delivering the results for each iteration to users, the proposed framework enables users to quickly acquire the information that the computational method provides as well as the ability to interact with them in real time. We show the benefits of the proposed framework by using various dimension reduction and clustering methods.
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    VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Changhyun ; Clarkson, Edward ; Liu, Zhicheng ; Lee, Hanseung ; Chau, Duen Horng ; Li, Fuxin ; Kannan, Ramakrishnan ; Stolper, Charles D. ; Inouye, David ; Mehta, Nishant ; Ouyang, Hua ; Som, Subhojit ; Gray, Alexander ; Stasko, John T. ; Park, Haesun
    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 a query processes for retrieval and active push that recommends the items of potential interest based on the user preferences. Equipped with efficient dynamic query interfaces for a large corpus of document data, VisIRR visualizes the retrieved documents in a scatter plot form with their overall topic clusters. At the same time, based on interactive personalized preference feedback on documents, VisIRR provides recommended documents reaching out to the entire corpus beyond the retrieved sets. Such recommended documents are represented in the same scatter space of the retrieved documents so that users can perform integrated analyses of both retrieved and recommended documents seamlessly. We describe the state-of-the-art computational methods that make these integrated and informative representations as well as real time interaction possible. We illustrate the way the system works by using detailed usage scenarios. In addition, we present a preliminary user study that evaluates the effectiveness of the system.
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    To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Daniel ; Dilkina, Bistra ; Zha, Hongyuan ; Park, Haesun
    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. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding micro-financial transactions available at Kiva. Based on this approach, we achieved a competitive performance of 0.84 AUC value in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan’s geolocation, a borrower’s gender, a field partner’s reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders’ background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results.
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    A Better World for All: Understanding and Promoting Micro-finance Activities in Kiva.org
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Changhyun ; Lee, Daniel ; Zha, Hongyuan ; Park, Haesun
    Micro-finance organizations provide non-profit loaning opportunities to eradicate poverty by financially equipping impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to those who have little. Kiva.org, a widely-used crowd-funded micro-financial service, provides researchers with an extensive amount of openly downloadable data containing a wealthy set of heterogeneous information regarding micro-financial transactions. Our objective is to identify the key factors that encourage people to make micro-financing donations, and ultimately, to keep them actively involved. In our contribution to further promote a healthy micro-finance ecosystem, we detail our personalized loan recommendation system which we formulate as a supervised learning problem where we try to predict how likely a given lender will fund a new loan. We construct the features for each data item by utilizing the available connectivity relationships in order to integrate all the available Kiva data types. For those lenders with no such relationships, e.g., first-time lenders, we propose a novel method of feature construction by computing joint nonnegative matrix factorization. By using a gradient boosting tree, a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows that our work is ready for use in practice. Finally, we reveal various interesting knowledge about lenders’ social behaviors in micro-finance activities.
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    Collaborative research: Development of effective gene selection algorithms
    (Georgia Institute of Technology, 2011-05-03) Park, Haesun ; Kim, Wooyoung ; Kim, Jingu ; Balasubramanian, Krishnakumar
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    Computational methods for nonlinear dimension reduction
    (Georgia Institute of Technology, 2010-11-30) Zha, Hongyuan ; Park, Haesun
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    Workshop on Future Direction in Numerical Algorithms and Optimization
    (Georgia Institute of Technology, 2008-01-15) Park, Haesun ; Golub, Gene ; Wu, Weili ; Du, Ding-Zhu
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    Sparse Nonnegative Matrix Factorization for Clustering
    (Georgia Institute of Technology, 2008) Kim, Jingu ; Park, Haesun
    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 as that of a lower rank approximation with special constraints allows comparisons between the constraints of NMF and K-means and provides the insight that some constraints can be relaxed from K-means to achieve NMF formulation. By introducing sparsity constraints on the coefficient matrix factor in NMF objective function, we in term can view NMF as a clustering method. We tested sparse NMF as a clustering method, and our experimental results with synthetic and text data shows that sparse NMF does not simply provide an alternative to K-means, but rather gives much better and consistent solutions to the clustering problem. In addition, the consistency of solutions further explains how NMF can be used to determine the unknown number of clusters from data. We also tested with a recently proposed clustering algorithm, Affinity Propagation, and achieved comparable results. A fast alternating nonnegative least squares algorithm was used to obtain NMF and sparse NMF.
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    Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons
    (Georgia Institute of Technology, 2008) Kim, Jingu ; Park, Haesun
    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 for NMF appears as a non-convex optimization problem, and various types of algorithms have been devised to solve the problem. The alternating nonnegative least squares (ANLS) framework is a block coordinate descent approach for solving NMF, which was recently shown to be theoretically sound and empirically efficient. In this paper, we present a novel algorithm for NMF based on the ANLS framework. Our new algorithm builds upon the block principal pivoting method for the nonnegativity constrained least squares problem that overcomes some limitations of active set methods. We introduce ideas to efficiently extend the block principal pivoting method within the context of NMF computation. Our algorithm inherits the convergence theory of the ANLS framework and can easily be extended to other constrained NMF formulations. Comparisons of algorithms using datasets that are from real life applications as well as those artificially generated show that the proposed new algorithm outperforms existing ones in computational speed.