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ORIE-4741-Course-Project

Exploring Novel Applications and Modifications to the "Information Sieve"

Kendrick Cancio (kdc57) Skylar Carfi (swc74)

In this project, we propose to explore novel applications and possibly improvements to the "Information Sieve" first described by Greg Ver Steeg and Adam Galsyan. The Information Sieve is a method for unsupervised learning that passes data through a series of progressively fine "sieves" where each layer of the sieve recovers a single latent factor that is maximally informative about multivariate dependence in the data. Current applications mentioned in the paper include its use in discrete Independent Component Analysis, lossy and lossless compression, and signal processing.

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