Classifying Partially Labeled Networked Data via Logistic Network Lasso
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 non-smooth 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Networked Information Aggregation for Binary Classification
We study networked binary classification on a directed acyclic graph (DAG) where each agent observes only a subset of the feature columns of a shared dataset. Agents act sequentially along the DAG: each receives predicti…
Binary ClassificationDeep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data
Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intra-class variatio…
BenchmarkingFood RecognitionGenerative Adversarial NetworkNetworked Agents in the Dark: Team Value Learning under Partial Observability
We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents. In contrast to previous methods that rely on complete state information or joint observations, our agents must learn…
Multi-agent Reinforcement LearningClassifying logistic vehicles in cities using Deep learning
Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monit…
Deep LearningClassifying Illegal Activities on Tor Network Based on Web Textual Contents
The freedom of the Deep Web offers a safe place where people can express themselves anonymously but they also can conduct illegal activities. In this paper, we present and make publicly available a new dataset for Darkne…