Authors:
Sangkyun Lee
and
Christian Pölitz
Affiliation:
Technische Universität Dortmund, Germany
Keyword(s):
Sensor Networks, Support Vector Machines, Gaussian Kernels, Sampling, Matrix Completion.
Related
Ontology
Subjects/Areas/Topics:
Kernel Methods
;
Matrix Factorization
;
Pattern Recognition
;
Similarity and Distance Learning
;
Theory and Methods
Abstract:
Recent developments in sensor technology allows for capturing dynamic patterns in vehicle movements, temperature
changes, and sea-level fluctuations, just to name a few. A usual way for decision making on sensor
networks, such as detecting exceptional surface level changes across the Pacific ocean, involves collecting
measurement data from all sensors to build a predictor in a central processing station. However, data collection
becomes challenging when communication bandwidth is limited, due to communication distance or
low-energy requirements. Also, such settings will introduce unfavorable latency for making predictions on
unseen events. In this paper, we propose an alternative strategy for such scenarios, aiming to build a consensus
support vector machine (SVM) in each sensor station by exchanging a small amount of sampled information
from local kernel matrices amongst peers. Our method is based on decomposing a “global” kernel defined
with all features into “local” kernels define
d only with attributes stored in each sensor station, sampling few
entries of the decomposed kernel matrices that belong to other stations, and filling in unsampled entries in
kernel matrices by matrix completion. Experiments on benchmark data sets illustrate that a consensus SVM
can be built in each station using limited communication, which is competent in prediction performance to an
SVM built with accessing all features.
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