Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Multi-scale Graph Fusion for Co-saliency Detection

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics

Novel AOPs-Based Dual-Environmental Digestion Method for Determination of Total Dissolved Nitrogen in Water

Based on a synergistic digestion method of ultraviolet combined with ozone (UV/O3), this article investigates the reaction characteristics of nitrogen-containing compounds (N-compounds) in water and the influence of ions on digestion efficiency. In this respect, a novel and efficient AOPs-based dual-environmental digestion method for the determination of total dissolved nitrogen (TDN) in waters with complex components is proposed, in the hopes of improving the detection efficiency and accuracy of total nitrogen via online monitoring. The results show that inorganic and organic N-compounds have higher conversion rates in alkaline and acidic conditions, respectively. Meanwhile, the experimental results on the influence of Cl−, CO32−, and HCO3− on the digestion process indicate that Cl− can convert to radical reactive halogen species (RHS) in order to promote digestion efficiency, but CO32− and HCO3− cause a cyclic reaction consuming numerous •OH, weakening the digestion efficiency. Ultimately, to verify the effectiveness of this novel digestion method, total dissolved nitrogen samples containing ammonium chloride, urea, and glycine in different proportions were digested under the optimal conditions: flow rate, 0.6 L/min; reaction temperature, 40 °C; pH in acidic conditions, 2; digestion time in acidic condition, 10 min; pH in alkaline conditions, 11; digestion time in alkaline conditions, 10 min. The conversion rate (CR) of samples varied from 93.23% to 98.64%; the mean CR was greater than 95.30%. This novel and efficient digestion method represents a potential alternative for the digestion of N-compounds in the routine analysis or online monitoring of water quality

One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace

This paper proposes a one-step spectral clustering method by learning an intrinsic affinity matrix (i.e., the clustering result) from the low-dimensional space (i.e., intrinsic subspace) of original data. Specifically, the intrinsic affinitymatrix is learnt by: 1) the alignment of the initial affinity matrix learnt from original data; 2) the adjustment of the transformation matrix, which transfers the original feature space into its intrinsic subspace by simultaneously conducting feature selection and subspace learning; and 3) the clustering result constraint, i.e., the graph constructed by the intrinsic affinity matrix has exact c connected components where c is the number of clusters. In this way, two affinity matrices and a transformation matrix are iteratively updated until achieving their individual optimum, so that these two affinity matrices are consistent and the intrinsic subspace is learnt via the transformation matrix. Experimental results on both synthetic and benchmark datasets verified that our proposed method outputted more effective clustering result than the previous clustering methods

Table_1_Peppermint extract improves egg production and quality, increases antioxidant capacity, and alters cecal microbiota in late-phase laying hens.docx

IntroductionPeppermint contains substantial bioactive ingredients belonging to the phytoestrogens, and its effects on the production of late-laying hens deserve more attention. This study evaluated the effects of dietary peppermint extract (PE) supplementation on egg production and quality, yolk fatty acid composition, antioxidant capacity, and cecal microbiota in late-phase laying hens.MethodPE powder was identified by UPLC-MS/MS analysis. Two hundred and sixteen laying hens (60 weeks old) were randomly assigned to four treatments, each for 28 days: (i) basal diet (control group, CON); (ii) basal diet + 0.1% PE; (iii) basal diet + 0.2% PE; and (iv) basal diet + 0.4% PE. Egg, serum, and cecal samples were collected for analysis.ResultsDietary PE supplementation increased the laying rate, serum triglyceride, immunoglobulin G, and total antioxidant capacity, while 0.2 and 0.4% PE supplementation increased eggshell thickness, serum total protein level, and superoxide dismutase activity of laying hens compared with the CON group (P DiscussionDietary PE supplementation improved egg production and quality (including yolk fatty acid composition) by increasing serum IgG and antioxidant capacity and modulating the intestinal microbiota in late-phase laying hens.</p