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Exemlpar-based Visualization of Large Document Corpus

Problem Definition

  • Information visualization is the interdisciplinary study of the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems, and the use of graphical techniques to help people understand and analyze data. In contrast with scientific visualization, information visualization focuses on abstract data sets, such as unstructured text or points in high-dimensional space, that do not have an inherent 2D or 3D geometrical structure. Multidimensional projection is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. Exemplar-based visualization is to visualize an extremely large data sets based on multidimensional projection technique, making the visualization highly efficient and flexible. In addition, the selected data exemplars through matrix approximation and decomposition neatly summarize the entire data set and greatly reduce the cognitive overload in the visualization, leading to an easier interpretation of large data sets.


  • Compute a representative text data subspace C (data exemplar set) and a low-rank approximation X by applying the low-rank matrix approximation method..
  • Data are clustered through the matrix decomposition: X=CWG^{T} , where W is the weight matrix, and G is the cluster indicator matrix. The soft cluster indicators G in the low-rank exemplar subspace, representing the probability of each data points proportion to the topics (classes).
  • Use Parameter Embedding method to embed data into a low-dimensional Euclidean space such that the input probabilities G are approximated as closely as possible by the embedding-space probabilities.


  • Visualize tens of thousands of documents with high accuracy (in retaining neighbor relations):
    Generally, the AC values obtained by the seven methods are higher for a small number of topics (e.g., z=3 topics in the following figure (a)) than those with a large number of topics (e.g., z=20 topics in the following figure 1(b)). Moreover, the accuracy achieved by the topic models (i.e., EV-844, EV-CUR, PLSA+PE and LSP) is significantly higher than the traditional projection methods (i.e., PCA, MDS and ISOMAP). These results indicate that topic information is very helpful for the data visualization. Another important observation from the following sub-figures is that EV-844 constantly provides a higher accuracy value than EV-CUR. This is mainly because Algorithm 844 selects unique columns (exemplars) while CUR may choose replicated ones to build the subspace. Finally, as shown in the figure (a), the two probabilistic topic models (i.e., EV and PLSA+PE) have comparable performance on 20Newsgroups-I (3 topics). However, as the number of topics increases, EV clearly outperforms PLSA+PE on 20Newsgroups-II (20 topics). These results imply that EV can appropriately embed documents in a two-dimensional Euclidean space while keeping the essential relationship of the documents, especially for a data set with a large number of topics.
  • Visualize tens of thousands of documents with high efficiency (in computation):
    We compare the computational speed of the six visualization methods: EV, PLSA+PE, PCA, LSP, MDS and ISOMAP in the following table. From the table, EV clearly is the quickest among the six, followed by PLSA+PE and PCA, while the computing time of LSP, MDS and ISOMAP increases quickly with the number of documents. More important, we observed that some algorithms fail to provide a result within a reasonable time for relatively large document sets. Specifically, ISOMAP is the slowest and cannot give a result when the matrix contains more than 3,000 documents due to insufficient memory. When we have more than 10,000 samples, only EV can provide a result within a reasonable computation time, while all other methods fail (indicated by a cross x in the table). Clearly, EV is suitable to visualize large text corpus we are increasingly facing these days thanks to its high computational efficiency.
  • Visualize tens of thousands of documents with high flexibility (through the use of exemplars):
    The following figure shows the visualization results obtained by EV, PLSA+PE, LSP, ISOMAP, MDS, and PCA on 20Newsgroups-I (subset of 20Newsgroups). Here, each point represents a document, and the different color shapes represent the topic labels. For example, there are three different color shapes in this figure, representing three groups of news: black diamond for "", green triangle for "" and red circle for "". In the EV visualization (figure (f)), documents with the same label are nicely clustered together while documents with different labels tend to be placed far away. In PLSA+PE and LSP (figures (e) and (d)), documents are located slightly more mixed than those in EV. On the other hand, with PCA, MDS and ISOMAP (figures (a)-(c)), documents with different labels are mixed, and thus the AC values of the corresponding layout are very low. Also, the figures (g)-(i), a series of visualization for 20Newsgroups-I are provided, from the most abstract view to the visual layout with considerate amount of details as the number of selected exemplars increases from 10 to 40. This result demonstrates that EV can use exemplars to summarize the distribution of the entire document collection.

Related Publications

  • Yanhua Chen, Lijun Wang, Ming Dong and Jing Hua, "Exemplar-based Visualization of Large Document Corpus", accepted by IEEE Information Visualization Conference (IEEE InfoVis), Atlantic City, NJ, USA, 2009 (acceptance rate = 26%, the journal version appeared in IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 15, No. 6, pp. 1161-1168, November/December 2009).


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