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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xianzhong Long Lei Chen Qun Li College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China

As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NMF), Graph regularized Non-negative Matrix Factorization (GNMF) and Locally Consistent Concept Factorization (LCCF). These methods possess respective strength and weakness. The common problem existing in these clustering algorithms is that they only use one kind of feature. However, different kinds of features complement each other and can be used to improve performance results. In this paper, in order to make use of the complementarity between different features, we propose an image representation method based on multi-features. Clustering results on several benchmark image data sets show that the proposed scheme outperforms some classical methods.

关键词： Clustering algorithms Image representation Feature extraction Optimization Symmetric matrices Machine learning algorithms Signal processing algorithms

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Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients 1

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20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

作者： Chen, Lei Zhang, Han Thung, Kim-Han Liu, Luyan Lu, Junfeng Wu, Jinsong Wang, Qian Shen, Dinggang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Radiology and BRIC University of North Carolina Chapel Hill United States School of Biomedical Engineering Med-X Research Institute Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner. © Springer International Publishing AG 2017.

关键词： Forecasting

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Identifying outliers in astronomical images with unsupervised machine learning

arXiv

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arXiv 2022年

作者： Han, Yang Zou, Zhiqiang Li, Nan Chen, Yanli School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose since designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE) + KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior to KNN (4 hours) and equal to CAE+KNN (10 minutes) for accomplishing the same task. Thus, we believe it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN to available survey datasets to assess its applicability and reliability. Copyright © 2022, The Authors. All rights reserved.

关键词： k-nearest neighbors

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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International Symposium on System and Software Reliability (ISSSR)

作者： Jinbo Zhao Jinling He Yuanhan Du Ming Tang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k.

关键词： Representation learning Accuracy Social networking (online) Heuristic algorithms Semantics Prediction algorithms Feature extraction

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Bidirectional LSTM-CRF for biomedical named entity recognition

Bidirectional LSTM-CRF for biomedical named entity recogniti...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xuemin Yang Zhihong Gao Yongmin Li Chuandi Pan Ronggen Yang Lejun Gong Geng Yang Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538680988;9781538680971

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition.

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

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Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

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Classifying Galaxy Morphologies with Few-Shot Learning

arXiv

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arXiv 2022年

作者： Zhang, Zhirui Zou, Zhiqiang Li, Nan Chen, Yanli College of Computer Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Science Beijing China

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human inspection. Consequently, machine learning, in particular supervised deep learning, has been widely employed to classify galaxy morphologies recently due to its exceptional automation, efficiency, and accuracy. However, supervised deep learning requires extensive training sets, which causes considerable workloads;also, the results are strongly dependent on the characteristics of training sets, which leads to biased outcomes potentially. In this study, we attempt Few-shot Learning to bypass the two issues. Our research adopts the dataset from Galaxy Zoo Challenge Project on Kaggle, and we divide it into five categories according to the corresponding truth table. By classifying the above dataset utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet, VGG 16, and ResNet 50 trained with different volumes of training sets separately, we find that few-shot learning achieves the highest accuracy in most cases, and the most significant improvement is 21% compared to AlexNet when the training sets contain 1000 images. In addition, to guarantee the accuracy is no less than 90%, few-shot learning needs ∼6300 images for training, while ResNet 50 requires ∼13000 images. Considering the advantages stated above, foreseeably, few-shot learning is suitable for the taxonomy of galaxy morphology and even for identifying rare astrophysical objects, despite limited training sets consisting of observational data only. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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VideoGIS data retrieval based on multi-feature fusion

VideoGIS data retrieval based on multi-feature fusion

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Haihong Dai Bin Hu Qian Cui Zhiqiang Zou College of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Geographic Science Nanjing Normal University Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) Ministry of Education Nanjing China

With the rapid development of smart cities and the advance of city safety and defense demands, the question that how to accurately discover and retrieve the data that users request from VideoGIS data faces a series of bottleneck problems. VideoGIS data retrieval is one of the important ways to solve above problems. In order to accelerate the rate of feature matching and improve the efficiency of the video retrieval, a new method of VideoGIS data retrieval based on multi-feature fusion is proposed in this paper. The method firstly use video frame difference based on Euclidean distance to extract the key frames under the spatial and temporal sampling of the video. On the basis of this, the global features (e.g. color, shape, texture) are fused by different weighted coefficients, then the feature vector, as the video multi-feature fusion representation, can be constructed by fusing the global features and local features. Based on the multi-feature fusion, correlation between video features is made full use. Compared with the method of single feature and two-feature fusion, the experimental results indicate that the proposed retrieval method has better retrieval effect.

关键词： Feature extraction Image color analysis data mining Correlation Euclidean distance Shape Streaming media

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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