Classification Scheme for Binary Data with Extensions

Denali Molitor; Deanna Needell; Aaron Nelson; Rayan Saab; Palina Salanevich

doi:10.1007/978-3-319-73074-5_4

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 proceeding › Chapter › Academic › peer-review

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.

Original language	English
Title of host publication	Compressed Sensing and Its Applications
Editors	Holger Boche, Giuseppe Caire, Robert Calderbank, Gitta Kutyniok, Rudolf Mathar, Philipp Petersen
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	129-151
Number of pages	23
ISBN (Electronic)	9783319730745
ISBN (Print)	9783319730738
DOIs	https://doi.org/10.1007/978-3-319-73074-5_4
Publication status	Published - 14 Aug 2019
Externally published	Yes

Publication series

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

Funding

Acknowledgements Molitor and Needell were partially supported by NSF CAREER grant #1348721 and NSF BIGDATA #1740325. Saab was partially supported by the NSF under DMS-1517204.

Access to Document

10.1007/978-3-319-73074-5_4

Molitor2019_Chapter_ClassificationSchemeForBinaryDFinal published version, 1.03 MB

Cite this

@inbook{0b3703dbc4bf40838a3120016ed805cc,

title = "Classification Scheme for Binary Data with Extensions",

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.",

author = "Denali Molitor and Deanna Needell and Aaron Nelson and Rayan Saab and Palina Salanevich",

year = "2019",

month = aug,

day = "14",

doi = "10.1007/978-3-319-73074-5_4",

language = "English",

isbn = "9783319730738",

series = "Applied and Numerical Harmonic Analysis",

publisher = "Springer",

pages = "129--151",

editor = "Holger Boche and { Caire}, Giuseppe and Robert Calderbank and Gitta Kutyniok and Rudolf Mathar and Philipp Petersen",

booktitle = "Compressed Sensing and Its Applications",

}

Classification Scheme for Binary Data with Extensions. / Molitor, Denali; Needell, Deanna; Nelson, Aaron et al.
Compressed Sensing and Its Applications. ed. / Holger Boche; Giuseppe Caire; Robert Calderbank; Gitta Kutyniok; Rudolf Mathar; Philipp Petersen. Cham, Switzerland: Springer, 2019. p. 129-151 (Applied and Numerical Harmonic Analysis).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Classification Scheme for Binary Data with Extensions

AU - Molitor, Denali

AU - Needell, Deanna

AU - Nelson, Aaron

AU - Saab, Rayan

AU - Salanevich, Palina

PY - 2019/8/14

Y1 - 2019/8/14

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85071433895&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-73074-5_4

DO - 10.1007/978-3-319-73074-5_4

M3 - Chapter

AN - SCOPUS:85071433895

SN - 9783319730738

T3 - Applied and Numerical Harmonic Analysis

SP - 129

EP - 151

BT - Compressed Sensing and Its Applications

A2 - Boche, Holger

A2 - Caire, Giuseppe

A2 - Calderbank, Robert

A2 - Kutyniok, Gitta

A2 - Mathar, Rudolf

A2 - Petersen, Philipp

PB - Springer

CY - Cham, Switzerland

ER -

Classification Scheme for Binary Data with Extensions

Abstract

Publication series

Funding

Access to Document

Other files and links

Fingerprint

Cite this