Browsing by Author "Tran, Nguyen"

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  • Classifying Big Data over Networks Via the Logistic Network Lasso
    (2019-02-19) Ambos, Henrik; Tran, Nguyen; Jung, Alexander
     A4 Artikkeli konferenssijulkaisussa
    We apply network Lasso to solve binary classification and clustering problems on network structured data. In particular we generalize ordinary logistic regression to non-Euclidean data defined over a complex network structure. The resulting logistic network Lasso classifier amounts to solving a convex optimization problem. A scalable classification algorithm is obtained by applying the alternating direction methods of multipliers.
  • Classifying Partially Labeled Networked Data VIA Logistic Network Lasso
    (2020-05) Tran, Nguyen; Ambos, Henrik; Jung, Alexander
     A4 Artikkeli konferenssijulkaisussa
    We apply the network Lasso to classify partially labeled data points which are characterized by high-dimensional feature vectors. In order to learn an accurate classifier from limited amounts of labeled data, we borrow statistical strength, via an intrinsic network structure, across the dataset. The resulting logistic network Lasso amounts to a regularized empirical risk minimization problem using the total variation of a classifier as a regularizer. This minimization problem is a nonsmooth convex optimization problem which we solve using a primal-dual splitting method. This method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.
  • A Network Compatibility Condition for Compressed Sensing over Complex Networks
    (2018-08-29) Tran, Nguyen; Ambos, Henrik; Jung, Alexander
     A4 Artikkeli konferenssijulkaisussa
    This paper continues our recently initiated line of work on analyzing the network Lasso (nLasso, which has been proposed as an efficient learning algorithm for massive networkstructured data sets (big data over networks). The nLasso extends the well-known Lasso estimator to network-structured datasets. In this paper we consider the nLasso using squared error loss and provide sufficient conditions on the network structure and available label information such that nLasso accurately recovers a clustered (piece-wise constant) graph signal (representing label information) from the information pro-vided by the labels of a few data points.
  • Semi-Supervised Learning over Complex Networks
    (2019-03-11) Ambos, Henrik
     Perustieteiden korkeakoulu | Master's thesis
    This work considers semi-supervised learning over network-structured datasets with an emphasis on modern convex optimization methods. As a case study, we investigate two specific variants of the Network Lasso problem: The Network Lasso and Logistic Network Lasso. We solve these using the Alternating Direction Method of Multipliers. Especially for the Logistic Network Lasso, we also give algorithms based on an inexact variant of ADMM and the primal-dual method. Our theoretical investigation is complemented with experiments conducted on artificial and real datasets.