Technical Program

Paper Detail

Paper Title Quantizing Signals for Linear Classification
Paper IdentifierTU2.R4.4
Authors Yahya H. Ezzeldin, Christina Fragouli, Suhas Diggavi, University of California, Los Angeles, United States
Session Testing and Classification II
Location Odéon, Level 3
Session Time Tuesday, 09 July, 11:40 - 13:00
Presentation Time Tuesday, 09 July, 12:40 - 13:00
Manuscript  Click here to download the manuscript
Abstract In many machine learning applications, once we have learned a classifier, in order to apply it, we may still need to gather features from distributed sensors over communication constrained channels. In this paper, we propose a polynomial complexity algorithm for feature quantization tailored to minimizing the classification error of a linear classifier. Our scheme produces scalar quantizers that are well-tailored to delay-sensitive applications, operates on the same training data used to learn the classifier, and allows each distributed sensor to operate independently of each other. Numerical evaluation indicates up to 65% benefits over alternative approaches. Additionally, we provide an example where, jointly designing the linear classifier and the quantization scheme, can outperform sequential designs.