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Discovery Projects - Grant ID: DP140101366

Title
Discovery Projects - Grant ID: DP140101366
Funding
ARC | Discovery Projects
Contract (GA) number
DP140101366
Start Date
2014/01/01
End Date
2016/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP140101366

 

  • Nonnegative Multi-level Network Factorization for Latent Factor Analysis

    Xuan, Junyu; Lu, Jie; Luo, Xiangfeng; Zhang, Guangquan (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP140101366 (DP140101366)
    Nonnegative Matrix Factorization (NMF) aims to factorize a matrix into two optimized nonnegative matrices and has been widely used for unsupervised learning tasks such as product recommendation based on a rating matrix. However, although networks between nodes with the same nature exist, standard NMF overlooks them, e.g., the social network between users. This problem leads to comparatively low recommendation accuracy because these networks are also reflections of the nature of the nodes, suc...

    Nonparametric Relational Topic Models through Dependent Gamma Processes

    Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Da Xu, Richard Yi; Luo, Xiangfeng (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP140101366 (DP140101366)
    Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparam...

    Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization

    Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Da Xu, Richard Yi (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP140101366 (DP140101366)
    The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly based on topic models. However, these approaches suffer from a serious issue in that the number of hidden topics/factors needs to be fixed in advance and an inappropriate number ma...

    Dependent Indian Buffet Process-based Sparse Nonparametric Nonnegative Matrix Factorization

    Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Da Xu, Richard Yi; Luo, Xiangfeng (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP140101366 (DP140101366)
    Nonnegative Matrix Factorization (NMF) aims to factorize a matrix into two optimized nonnegative matrices appropriate for the intended applications. The method has been widely used for unsupervised learning tasks, including recommender systems (rating matrix of users by items) and document clustering (weighting matrix of papers by keywords). However, traditional NMF methods typically assume the number of latent factors (i.e., dimensionality of the loading matrices) to be fixed. This assumptio...

    Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process

    Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Da Xu, Richard Yi; Luo, Xiangfeng (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP140101366 (DP140101366)
    Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of t...
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