Eigenfaces, Data Visualizations and Clustering Methods

Today’s meeting covered everything from classifying whether a certain photo is of Justin Bieber using  linear algebra, to understanding how call steering portals really work.

Eigenfaces: Samson explained through principal component analysis, how eigenfaces can be used in face recognition.

Data Visualization: Samson also talked about information density, the data-ink ratio and how to condense visualizations without sacrificing information. He described several methods including sparklines, one of Tufte’s inventions, as well as why the “ideal” length of a x-y graph is dependent on the rate of change of the trendline.

Semantic Clustering: Konrad gave an overview of some of the work he did at Nuance involving semantic and k-means clustering algorithms. He described how the randomness of customer calls, among other variables, makes it difficult to know exactly the appropriate semantic tags one should use in large data sets.

Next meeting will be next Tuesday Nov 29th at 3pm in the Stats Club Office M3 3109.

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