How many bits from how many sensors? A trade-off in distributed nearest-neighbor learning

Author

Marano, Stefano ; Matta, Vincenzo ; Willett, P.

Author_Institution

Dept. of Inf. & Electr. Eng. & Appl. Math., Univ. of Salerno, Fisciano, Italy

fYear

2014

fDate

4-9 May 2014

Firstpage

7619

Lastpage

7623

Abstract

In one of his landmark papers, Cover established the fundamental scaling laws of learning with nearest-neighbor rules (T.M. Cover, 1968). With the recent advances on distributed nearest-neighbor learning in sensor networks novel trade-offs arise, involving the faithfulness of message representation (quantization bits) and the number of delivered messages (transmitting sensors). This is the main theme of this paper.

Keywords

learning (artificial intelligence); quantisation (signal); wireless sensor networks; distributed nearest-neighbor learning; fundamental scaling laws; message representation; nearest-neighbor rules; quantization bits; sensor networks; transmitting sensors; Approximation methods; Artificial neural networks; Estimation; Limiting; Quantization (signal); Sensors; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on

Conference_Location

Florence

Type

conf

DOI

10.1109/ICASSP.2014.6855082

Filename

6855082

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=180392