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Quantization and Compressive Learning Methods for Omics Data

Focus Compression

Project Goal

To develop new omics data quantization and lossless compression algorithms and accompanying machine learning methods that are robust to quantization errors.

Results & Resources

Matrix factorization (MF) is a versatile learning method that has found wide applications, but many algorithms do not adequately scale with the size of available datasets. The Milenkovic lab developed an online convex MF algorithm that maintains a collection of constantly sized sets of representative data samples, as described in their preprint.


Lead Investigator

Olgica Milenkovic
Olgica Milenkovic