21,213 research outputs found

    Multi-graph learning

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    University of Technology Sydney. Faculty of Engineering and Information Technology.Multi-instance learning (MIL) is a special learning task where labels are only available for a bag of instances. Although MIL has been used for many applications, existing MIL algorithms cannot handle complex data objects, and all require that instances inside each bag are represented as feature vectors (e.g. being represented in an instance-feature format). In reality, many real-world objects are inherently complicated, and an object can be represented as multiple instances with dependency structures (i.e. graphs). Such dependency allows relationships between objects to play important roles, which, unfortunately, remain unaddressed in traditional instance-feature representations. Motivated by the challenges, this thesis formulates a new multi-graph learning paradigm for representing and classifying complicated objects. With the proposed multi-graph representation, the thesis systematically addresses several key learning tasks, including Multi-Graph Learning: A graph bag contains one or multiple graphs, and each bag is labeled as either positive or negative. The aim of multi-graph learning is to build a learning model from a number of labeled training bags to predict previously unseen bags with maximum accuracy. To solve the problem, we propose two types of approaches: 1) Multi-Graph Feature based Learning (gMGFL) algorithm that explores and selects an optimal set of subgraphs as features to transfer each bag into a single instance for further learning; and 2) Boosting based Multi-Graph Classification framework (bMGC), which employs dynamic weight adjustment, at both graph- and bag-levels, to select one subgraph in each iteration to form a set of weak graph classifiers. Multi-Instance Multi-Graph learning: A bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. In the thesis, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative subgraphs discovery process, and (2) embed discovered subgraphs into the instance feature selection process. Positive and Unlabeled Multi-Graph Learning: The training set only contains positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features available to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. Multi-Graph-View Learning: A multi-graph-view model utilizes graphs constructed from multiple graph-views to represent an object. In our research, we formulate a new multi-graph-view learning task for graph classification, where each object to be classified is represented graphs under multi-graph-view. To solve the problem, we propose a Cross Graph-View Subgraph Feature based Learning (gCGVFL) algorithm that explores an optimal set of subgraph features cross multiple graph-views. In addition, a bag based multi-graph model is further used to relax the labeling by only requiring one label for each graph bag, which corresponds to one object. For learning classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), to explore subgraphs from multiple graph-views for learning. Experiments on real-world data validate and demonstrate the performance of proposed methods for classifying complicated objects using multi-graph learning

    Structure fusion based on graph convolutional networks for semi-supervised classification

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    Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

    Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

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    Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

    Multi-Target Prediction: A Unifying View on Problems and Methods

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    Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research
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