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Bryan Smith

Dimensionality Reduction: How to Find the Important Variables for your Visualization

Real world data typically contain a large number of variables, many of which are not relevant to the analysis and visualization task at hand.  It is therefore critically important to understand how to identify the most relevant variables for your task.  This workshop will provide a brief introduction to the concepts of feature selection and feature extraction as applied to large, noisy data analysis and visualization problems.

Bryan Smith, Quid
Bryan earned his PhD in Computational Neuroscience from Caltech where he studied the physical mechanisms of information encoding in neural circuits.  At Quid, Bryan is applying semi-supervised learning algorithms to develop metrics for ranking public and private entities with respect to the semantic context of client queries.Prior to working at Quid, Bryan built and managed the R&D group at EmSense, a Neuromarketing company in San Francisco.

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