MAP IT to Visualize Representations

Robert Jenssen

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Keywords: Visualization; Representation learning; Dimensionality reduction; Divergence

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TL;DR: MAP IT is a new method for visualization by dimensionality reduction using a projective divergence measure as cost function

Abstract: MAP IT visualizes representations by taking a fundamentally different approach to dimensionality reduction. MAP IT aligns distributions over discrete marginal probabilities in the input space versus the target space, thus capturing information in local regions, as opposed to current methods which align based on individual probabilities between pairs of data points (states) only. The MAP IT theory reveals that alignment based on a projective divergence avoids normalization of weights (to obtain true probabilities) entirely, and further reveals a dual viewpoint via continuous densities and kernel smoothing. MAP IT is shown to produce visualizations which capture class structure better than the current state of the art while being inherently scalable.

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Primary Area: visualization or interpretation of learned representations

Submission Number: 4991