Classification Scheme for Binary Data with Extensions
Publication date
2019-08-14
Authors
Molitor, Denali
Needell, Deanna
Nelson, Aaron
Saab, Rayan
Salanevich, Palina
Editors
Boche, Holger
Caire, Giuseppe
Calderbank, Robert
Kutyniok, Gitta
Mathar, Rudolf
Petersen, Philipp
Advisors
Supervisors
Document Type
Part of book
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Abstract
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.
Keywords
Taverne, Applied Mathematics
Citation
Molitor, D, Needell, D, Nelson, A, Saab, R & Salanevich, P 2019, Classification Scheme for Binary Data with Extensions. in H Boche, G Caire, R Calderbank, G Kutyniok, R Mathar & P Petersen (eds), Compressed Sensing and Its Applications. Applied and Numerical Harmonic Analysis, Springer, Cham, Switzerland, pp. 129-151. https://doi.org/10.1007/978-3-319-73074-5_4