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| 1 | LTSA-LE:A Local Tangent Space Alignment Label Enhancement Algorithm显示文摘According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy. | Chao Tan Genlin Ji Richen Liu Yanqiu Cao | 2021 | Tsinghua Science and Technology2021，26，2： | 2 |
| 2 | A review of researches on spatial clustering显示文摘 | LIU Sheng JI Genlin | 2010 | Journal of Nanjing Normal University(Engineering and Technology Edition)2010，，2： | 1 |
| 3 | A Survey of Multi-Space Techniques in Spatio-Temporal Simulation Data Visualization显示文摘The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.). | Xueyi Chen Liming Shen Ziqi Sha Richen Liu Siming Chen Genlin Ji Chao Tan | 2019 | Visual Informatics2019，3，3： | 0 |
| 4 | LKLR: A Local Tangent Space-Alignment Kernel Least-Squares Regression Algorithm显示文摘In the fields of machine learning and data mining, label learning is a nascent area of research, and within this paradigm, there is much room for improving multi-label manifold learning algorithms for high-dimensional data. Thus far, researchers have experimented with mapping relationships from the feature space to the traditional logical label space(using neighbors in the label space, for example, to predict logical label vectors from the feature space's manifold structure). Here we combine the feature manifold's and label space's local topological structures to reconstruct the label manifold. To achieve this, we use a nonlinear manifold learning algorithm to transform the local topological structure from the feature space to the label space. Our algorithm adopts a regularized leastsquares kernel method to realize the reconstruction process, employing an optimization function to find the best solution. Extensive experiments show that our algorithm significantly improves multi-label manifold learning in terms of learning accuracy and time complexity. | Chao Tan Genlin Ji | 2019 | Tsinghua Science and Technology2019，24，4： | 0 |
| 5 | An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams显示文摘The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient. | Yujie Zhang Genlin Ji Bin Zhao Bo Sheng | 2019 | Computers, Materials & Continua2019，，6： | 0 |
| 6 | A Multi-Label Classification Method for Vehicle Video显示文摘In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity scene,one of the major challenges is mining information comprehensively from massive features in vehicle video.This paper proposes a multi-label classification method MCM-VV(Multi-label Classification Method for Vehicle Video)for vehicle video to judge the label of road condition for unmanned system.Method MCM-VV includes a process of feature extraction and a process of multi-label classification.During feature extraction,grayscale,lane line and the edge of main object are extracted after video preprocessing.During the multi-label classification,the algorithm DR-ML-KNN(Multi-label K-nearest Neighbor Classification Algorithm based on Dimensionality Reduction)learns the training set to obtain multi-label classifier,then predicts the label of road condition according to maximum a posteriori principle,finally outputs labels and adds the new instance to training set for the optimization of classifier.Experimental results on five vehicle video datasets show that the method MCM-VV is effective and efficient.The DR-ML-KNN algorithm reduces the runtime by 50%.It also reduces the time complexity and improves the accuracy. | Yanqiu Cao Chao Tan Genlin Ji | 2020 | Journal on Big Data2020，2，1： | 0 |
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