Joint Cuts and Matching of Partitions in One Graph

Yu, Tianshu; Yan, Junchi; Zhao, Jieyi; Li, Baoxin

doi:10.48550/arxiv.1711.09584

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…The experiments on three benchmark data sets show its advantages. In the future, we plan to apply the proposed method other applications, such as biometrics [20,27], network analysis [24], human pose estimation [11,10,9], mobile computing [16,8], mathematics [17,2,1,18], etc. The similar approach can also be adopted in other related fields such as systems [6,7,14], and system security [4,5,22].…”

Section: Discussionmentioning

confidence: 99%

Cross-Domain Attribute Representation Based on Convolutional Neural Network

Zhang

Liang

et al. 2018

Intelligent Computing Methodologies

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In the problem of domain transfer learning, we learn a model for the prediction in a target domain from the data of both some source domains and the target domain, where the target domain is in lack of labels while the source domain has sufficient labels. Besides the instances of the data, recently the attributes of data shared across domains are also explored and proven to be very helpful to leverage the information of different domains. In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and attributes. We proposed to embed the attributes of different domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by different domains by matching across domains, and a domain-specific CNN model to represent the information of each domain. The concatenation of the three CNN model outputs is used to predict the class label. An iterative algorithm based on gradient descent method is developed to learn the parameters of the model. The experiments over benchmark datasets show the advantage of the proposed model.

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