Classification Scheme for Binary Data with Extensions

Denali Molitor, Deanna Needell, Aaron Nelson*, Rayan Saab, Palina Salanevich

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


In this chapter, we present a simple classification scheme that utilizes only 1-bit measurements of the training and testing data. Our method is intended to be efficient in terms of computation and storage while also allowing for a rigorous mathematical analysis. After providing some motivation, we present our method and analyze its performance for a simple data model. We also discuss extensions of the method to the hierarchical data setting, and include some further implementation considerations. Experimental evidence provided in this chapter demonstrates that our methods yield accurate classification on a variety of synthetic and real data.

Original languageEnglish
Title of host publicationCompressed Sensing and Its Applications
EditorsHolger Boche, Giuseppe Caire, Robert Calderbank, Gitta Kutyniok, Rudolf Mathar, Philipp Petersen
Place of PublicationCham, Switzerland
Number of pages23
ISBN (Electronic) 9783319730745
ISBN (Print)9783319730738
Publication statusPublished - 14 Aug 2019
Externally publishedYes

Publication series

NameApplied and Numerical Harmonic Analysis
ISSN (Print)2296-5009
ISSN (Electronic)2296-5017


Dive into the research topics of 'Classification Scheme for Binary Data with Extensions'. Together they form a unique fingerprint.

Cite this