52,764 research outputs found
An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications
Transfer learning, or domain adaptation, is concerned with machine learning
problems in which training and testing data come from possibly different
probability distributions. In this work, we give an information-theoretic
analysis on the generalization error and excess risk of transfer learning
algorithms, following a line of work initiated by Russo and Xu. Our results
suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence
plays an important role in the characterizations where and
denote the distribution of the training data and the testing test,
respectively. Specifically, we provide generalization error upper bounds for
the empirical risk minimization (ERM) algorithm where data from both
distributions are available in the training phase. We further apply the
analysis to approximated ERM methods such as the Gibbs algorithm and the
stochastic gradient descent method. We then generalize the mutual information
bound with -divergence and Wasserstein distance. These generalizations
lead to tighter bounds and can handle the case when is not absolutely
continuous with respect to . Furthermore, we apply a new set of
techniques to obtain an alternative upper bound which gives a fast (and
optimal) learning rate for some learning problems. Finally, inspired by the
derived bounds, we propose the InfoBoost algorithm in which the importance
weights for source and target data are adjusted adaptively in accordance to
information measures. The empirical results show the effectiveness of the
proposed algorithm.Comment: 47 pages, 6 figure
Communication Theoretic Data Analytics
Widespread use of the Internet and social networks invokes the generation of
big data, which is proving to be useful in a number of applications. To deal
with explosively growing amounts of data, data analytics has emerged as a
critical technology related to computing, signal processing, and information
networking. In this paper, a formalism is considered in which data is modeled
as a generalized social network and communication theory and information theory
are thereby extended to data analytics. First, the creation of an equalizer to
optimize information transfer between two data variables is considered, and
financial data is used to demonstrate the advantages. Then, an information
coupling approach based on information geometry is applied for dimensionality
reduction, with a pattern recognition example to illustrate the effectiveness.
These initial trials suggest the potential of communication theoretic data
analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan.
201
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