Learning from Heterogeneous Data Based on Social Interactions over
Graphs
- URL: http://arxiv.org/abs/2112.09483v1
- Date: Fri, 17 Dec 2021 12:47:18 GMT
- Title: Learning from Heterogeneous Data Based on Social Interactions over
Graphs
- Authors: Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed
- Abstract summary: This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions.
We show that the.
strategy enables the agents to learn consistently under this highly-heterogeneous setting.
We show that the.
strategy enables the agents to learn consistently under this highly-heterogeneous setting.
- Score: 58.34060409467834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work proposes a decentralized architecture, where individual agents aim
at solving a classification problem while observing streaming features of
different dimensions and arising from possibly different distributions. In the
context of social learning, several useful strategies have been developed,
which solve decision making problems through local cooperation across
distributed agents and allow them to learn from streaming data. However,
traditional social learning strategies rely on the fundamental assumption that
each agent has significant prior knowledge of the underlying distribution of
the observations. In this work we overcome this issue by introducing a machine
learning framework that exploits social interactions over a graph, leading to a
fully data-driven solution to the distributed classification problem. In the
proposed social machine learning (SML) strategy, two phases are present: in the
training phase, classifiers are independently trained to generate a belief over
a set of hypotheses using a finite number of training samples; in the
prediction phase, classifiers evaluate streaming unlabeled observations and
share their instantaneous beliefs with neighboring classifiers. We show that
the SML strategy enables the agents to learn consistently under this
highly-heterogeneous setting and allows the network to continue learning even
during the prediction phase when it is deciding on unlabeled samples. The
prediction decisions are used to continually improve performance thereafter in
a manner that is markedly different from most existing static classification
schemes where, following training, the decisions on unlabeled data are not
re-used to improve future performance.
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