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40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Lan, Long Guan, Naiyang Zhang, Xiang Huang, Xuhui Luo, Zhigang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467369978

Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, LP assumes nearby examples should share the same label, thus, it unavoidably pushes the labels to the wrong examples, especially when different la- beled examples are not strictly separated. Seed K-means uses labeled examples to initialize class centers, and avoid getting stuck in poor local optima comparing to traditional K-means, however the hard constraint of each example's membership makes Seed K-means failed in many real world applications. This paper proposes a novel label walking nonnegative matrix factorization method (LWNMF) to handle labeled examples in SSL based on the framework of NMF. LWNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix, and to travel labels to unlabeled examples, LWNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. Since LWNMF learns comprehensive class centroids, labels iteratively walk to unlabeled examples through these significant centroids. © 2015 IEEE.

关键词： K-means Label propagation Nonnegative matrix factorization Semisupervised learning

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NVLAN: A Novel VLAN Technology for Scalable Multi-tenant Datacenter Networks 2

NVLAN: A Novel VLAN Technology for Scalable Multi-tenant Dat...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Hu, Yang Li, Huiba Peng, Yuxing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Computer Science Department China

ISBN: (纸本)9781479980857

Nowadays, cloud providers of 'Infrastructure as a service' require datacenter networks to support virtualization and multi-tenancy at large scale, while it brings a grand challenge to datacenters. Traditional network technologies of datacenters and common commodity devices of network can not meet these new needs. In this paper, we present NVLAN, a novel VLAN technology for scalable multi-tenant datacenter networks. NVLAN eliminates the quantity limitation of logic isolated networks on VLAN through a slight modification. NVLAN can be applied to very large-scale datacenter networks, without the need of expensive and dedicated switches. Through encapsulation of tenant's packets, NVLAN provides tenants with full address space virtualization to configure their own logic network. We implemented NVLAN on a test bed, and demonstrated that it can be scaled up to large numbers of multi-tenant networks with negligible overhead. © 2014 IEEE.

关键词： Virtualization

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A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees

A mobile recommendation system based on logistic regression ...

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Yaozheng Feng, Dawei Li, Dongsheng Chen, Xinyuan Zhao, Yunxiang Niu, Xin National Lab for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410003 China

ISBN: (纸本)9781509006199

Real-life behaviors shown by the mobile users typically exhibit plenty noises, making it hard to construct an effective recommendation engine. In this paper, we present a fused model based on the LR algorithm and the GBDT algorithm to recommend vertical industry commodities in a mobile setting. A set of specifically designed methods are proposed to deal with the data preprocessing and feature extraction problem for the mobile recommendation scenario. The proposed method is evaluated on a large scale real-world dataset provided by the Alibaba mobile shopping department. Result on the F1 score has seen an improvement of 2%-36% compared with the baseline. © 2016 IEEE.

关键词： Recommender systems

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Stochastic extra-gradient based alternating direction methods for graph-guided regularized minimization

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Frontiers of Information Technology & Electronic Engineering 2018年第6期19卷 755-762页

作者： Qiang LAN Lin-bo QIAO Yi-jie WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

In this study, we propose and compare stochastic variants of the extra-gradient alternating direction method, named the stochastic extra-gradient alternating direction method with Lagrangian function（SEGL） and the stochastic extra-gradient alternating direction method with augmented Lagrangian function（SEGAL）, to minimize the graph-guided optimization problems, which are composited with two convex objective functions in large scale.A number of important applications in machine learning follow the graph-guided optimization formulation, such as linear regression, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regression. We conduct experiments on fused logistic regression and graph-guided regularized regression. Experimental results on several genres of datasets demonstrate that the proposed algorithm outperforms other competing algorithms, and SEGAL has better performance than SEGL in practical use.

关键词： Stochastic optimization Graph-guided minimization Extra-gradient method Fused logistic regression Graph-guided regularized logistic regression

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

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Efficient social network approximate analysis on blogosphere based on network structure characteristics

Efficient social network approximate analysis on blogosphere...

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3rd Workshop on Social Network Mining and Analysis, SNA-KDD '09

作者： Tang, Jintao Wang, Ting Wang, Ji Wei, Dengping School of Computer National University of Defense Technology ChangSha Hunan 410073 China National Laboratory for Parallel and Distributed Processing ChangSha Hunan 410073 China

ISBN: (纸本)9781605586762

As a typical social media in Web 2.0, blogs have attracted a surge of researches. Unlike the traditional studies, the social networks mined from Internet are very large, which makes a lot of social network analyzing algorithms to be intractable. According to this phenomenon, this paper addresses the novel problem of efficient social networks analyzing on blogs. This paper turns to account the structural characteristics of real large-scale complex networks, and proposes a novel shortest path approximate algorithm to calculate the distance and shortest path between nodes efficiently. The approximate algorithm then is incorporated with social network analysis algorithms and measurements for large-scale social networks analysis. We illustrate the advantages of the approximate analysis through the centrality measurements and community mining algorithms. The experiments demonstrate the effectiveness of the proposed algorithms on blogs, which indicates the necessity of taking account of the structural characteristics of complex networks when optimizing the analysis algorithms on large-scale social networks. Copyright 2009 ACM.

关键词： Blogs

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Minimum redundancy gene selection based on grey relational analysis

Minimum redundancy gene selection based on grey relational a...

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作者： Zhang, Li-Juan Li, Zhou-Jun Chen, Huo-Wang Wen, Jian National Laboratory for Parallel and Distributed Processing Changsha 410073 China School of Computer Science and Engineering Beihang University Beijing 100083 China

ISBN: (纸本)0769527027

In this article we describe a method for selecting informative genes from microarray data. The method is based on clustering, namely, it first find similar genes, group them and then select informative genes from these groups to avoid redundancy. A new gene similarity measure based on Grey Relational Analysis (GRA), called Grey Relational Grade (GRG), is used in clustering. Experiments on three public data sets demonstrate the effectiveness of our method. © 2006 IEEE.

关键词： Redundancy

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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An efficient packing algorithm for spatial keyword queries

An efficient packing algorithm for spatial keyword queries

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6th International Conference on Convergence and Hybrid Information Technology, ICHIT 2012

作者： Pan, Jinkun Li, Dongsheng Li, Liming National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology 410073 Changsha China Shanghai Garrision 200040 Shanghai China

ISBN: (纸本)9783642326448

With the increase of location-based services, Web contents are being geo-tagged, and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. Unfortunately, the state-of-the-art packing algorithms only focus on spatial relationship, and they are not fit for the spatial keyword queries. In this paper, we propose a new packing algorithm named KBS which takes both location and keyword information into consideration, thus optimizing R-tree for spatial keyword queries. Experimental results on both real and synthetic datasets show that our method achieves high performance and space utilization. © 2012 Springer-Verlag.

关键词： Location based services

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Skyline query processing on interval uncertain data

Skyline query processing on interval uncertain data

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作者： Li, Xiaoyong Wang, Yijie Li, Xiaoling Wang, Guangdong National Lab. for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769546698

Many recent applications involve processing and analyzing uncertain data. Recently, several research efforts have addressed answering skyline queries efficiently on massive uncertain datasets. However, the research lacks methods to compute these queries on uncertain data, where each dimension of the uncertain object is represented as an interval or an exact value. In this paper, we extensively study the problem of skyline query on these interval based uncertain objects, which has never been studied before. We first model the problem of querying the skylines on interval datasets. Typically, we address two efficient algorithms with I/O optimal for the conventional interval skyline queries and constrained interval skyline queries, respectively. Extensive experiments demonstrate the efficiency of all our proposed algorithms. © 2012 IEEE.

关键词： Query processing

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