David G. UnderhillLuke K. McDowell - PowerPoint PPT Presentation

Title: David G. UnderhillLuke K. McDowell


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Enhancing Text Analysis via Dimensionality
Reduction

• David G. Underhill Luke K. McDowell
• Computer Science Department, United States Naval
  Academy

David J. Marchette Jeffrey L. Solka Naval
Surface Warfare Center, Dahlgren Division
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How to make sense of an overwhelming amount of
data?
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How to make sense of an overwhelming amount of
data?

• Can dimensionality reduction help?

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Outline

• Problem Statement
• Background
• Text Mining Process
• Dimensionality Reduction
• Experimental Analysis
• Classification
• Contributions and Conclusions
• Future Work

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Text Mining Overview
Distance Matrix
Term Document Matrix
Encode
Compare
Analyze
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Text Mining Overview
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Dimensionality Reduction (DR)

• Goal simplify a complex data set in a way that
  preserves meanings inherent in the original data
• Usually applied to geometric or numerical data

• How can DR improve text mining?
• May reveal patterns obscured in the original data
• Improves analysis time over the original, larger
  data
• Greatly decreases storage and transmission costs

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Outline

• Problem Statement
• Background
• Experimental Analysis
• Experimental Question and Method
• Task 1 Classification
• Nearest Neighbor Classifier
• Linear Classifier
• Quadratic Classifier
• Contributions and Conclusions
• Future Work

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Experimental Question

• Can DR improve text mining performance?
• Many valid DR approaches
• Relative DR performance unknown for textual data

Ultimate Goal Identify DR techniques that best
facilitate text mining.
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Experimental Method

• Evaluate 5 DR methods
• Linear
• 1) PCA (Principal Components Analysis)
• 2) MDS (Multidimensional Scaling)
• Non-Linear
• 3) Isomap
• 4) LLE (Locally Linear Embedding)
• 5) LDM (Lafons Diffusion Maps)
• Baseline
• None-Sort original features sorted by average
  weight
• Evaluate 3 classifiers
• 1) Nearest Neighbor
• 2) Linear
• 3) Quadratic
• Evaluate 3 data sets
• 1) Science News
• 2) Google News

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Outline

• Problem Statement
• Background
• Experimental Analysis
• Experimental Question
• Classification
• Nearest Neighbor Classifier
• Linear Classifier
• Quadratic Classifier
• Contributions and Conclusions
• Future Work

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Classification

• Labeling documents with known categories based on
  training data

Assessment accuracy of category assignments
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k-Nearest Neighbor Classifier

• Assign category based on k nearest neighbors
• Most frequent category is assigned
• k 9 used for following graphs
• Trends similar for other values

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kNN Classifier on Science News
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kNN Classifier on Google News
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kNN Classifier on Science Technology
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Linear Classifier

• Assign category based on a linear combination of
  features
• Assumes features are
• normally distributed
• Results for the quadratic classifier,
• which doesnt make this assumption,
• were comparable

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Linear Classifier on Science News
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Linear Classifier on Google News
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Linear Classifier on Science Technology
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Outline

• Problem Statement
• Background
• Experimental Analysis
• Contributions and Conclusions
• Future Work

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Classification Results

• Applying DR improves accuracy versus not applying
  DR for a fixed number of dimensions

• Best DR techniques achieve high accuracy in few
  dimensions

• MDS Isomap yield the most consistent and
  reliable results
• This advantage is more pronounced on difficult
  corpuses
• Contradicts van der Maaten et al. 2007 results
  show PCA best, but only evaluates one textual
  data set
• PCA is good, but not the best it suffers on
  harder data sets

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Outline

• Problem Statement
• Background
• Experimental Analysis
• Contributions and Conclusions
• Future Work

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Future Work

• More precisely characterize MDS, Isomap advantage
• Investigate other classification methods
• Evaluate data sets with different kinds of
  information

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Acknowledgements

• Trident Scholar Research Program
• Office of Naval Research

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Enhancing Text Analysis via Dimensionality
Reduction

• David G. Underhill Luke K. McDowell
• Computer Science Department, United States Naval
  Academy

David J. Marchette Jeffrey L. Solka Naval
Surface Warfare Center, Dahlgren Division
27
2-Dimensional Visualizations

• Reduction to just 2 dimensions
• Easy visualization graph on Cartesian plot
• Each point is colored according to its category
• Assess quality of separation with best 2
  dimensions
• Highlight areas of confusion

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2D Visualization of Science News (2-cat)
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2D Visualization of Science News (8-cat)
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2D Visualization of Google News
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2D Visualization of Science Technology
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kNN Classifier on Science News (2-category)
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kNN Classifier on Science News (4-OL)