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- W2800451211 abstract "We apply the network to solve binary classification and clustering problems for network-structured data. To this end, we generalize ordinary regression to non-Euclidean data with an intrinsic network structure. The resulting logistic network Lasso amounts to solving a non-smooth convex regularized empirical risk minimization. The risk is measured using the loss incurred over a small set of labeled nodes. For the regularization, we propose to use the total variation of the classifier requiring it to conform to the underlying network structure. A scalable implementation of the learning method is obtained using an inexact variant of the alternating direction methods of multipliers which results in a scalable learning algorithm" @default.
- W2800451211 created "2018-05-17" @default.
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- W2800451211 date "2018-05-07" @default.
- W2800451211 modified "2023-09-27" @default.
- W2800451211 title "Classifying Big Data over Networks via the Logistic Network Lasso" @default.
- W2800451211 hasPublicationYear "2018" @default.
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