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unsupervised feature selection via joint local learning and group sparse regression

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Frontiers of Information Technology & Electronic Engineering 2019年第4期20卷 538-553页

作者： Yue WU Can WANG Yue-qing ZHANG Jia-jun BU Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science and Technology Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies

Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However,due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression(JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets(images, voices, and web pages) demonstrate the effectiveness of JLLGSR.

关键词： unsupervised Local learning Group sparse regression Feature selection

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unsupervised learning on scientific ocean drilling datasets from the South China Sea

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Frontiers of Earth Science 2019年第1期13卷 180-190页

作者： Kevin C.TSE Hon-Chim CHIU Man-Yin TSANG Yiliang LI Edmund Y.LAM Department of Earth Sciences The University of Hong KongPokfulamHong KongChina Department of Geography and Centre for Geo-computation Studies Hong Kong Baptist UniversityKowloon TongHong KongChina Department of Earth Sciences University of TorontoTorontoON M5S 2M8Canada Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong KongChina

unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea.Compared to studies on similar datasets,but using supervised learning methods which are designed to make predictions based on sample training data,unsupervised learning methods require no a priori information and focus only on the input data.In this study,popular unsupervised learning methods including K-means,self-organizing maps,hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters.The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales.K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales.This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

关键词： machine learning unsupervised learning ODP IODP clustering

An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

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Chinese Journal of Aeronautics 2023年第5期36卷 434-446页

作者： Shurong YUAN Lei SHI Yutong ZHAI Bo YAO Fangyan LI Yuefan DU Key Laboratory of Equipment Efficiency in Extreme Environment Ministry of EducationXiDian UniversityXi’an 710071China School of Aerospace Science and Technology XiDian UniversityXi’an 710071China

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years,the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper,which is based on the Hyperspectral Features(HFs)of hypersonic targets covered with plasma sheath during high-speed flight.First,a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs.Then,the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar.Finally,an unsupervised classification method based on the Density Peak Clustering(DPC)for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy.The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set.The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy(OA),Average Accuracy(AA)and Kappa coefficient.

关键词： Classification Clustering algorithms Hypersonic vehicles Hyperspectral unsupervised

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Hyperspectral Image Classification Based on unsupervised Heterogeneous Domain Adaptation CycleGan

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Chinese Journal of Electronics 2020年第4期29卷 608-614页

作者： WANG Xuesong LI Yiran CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

Aiming at the difficulty of obtaining sufficient labeled Hyperspectral image(HSI) data and the inconsistent feature distribution of different HSIs, a novel unsupervised heterogeneous domain adaptation CycleGan(UHDAC) is proposed by using CycleGan to capture the transferable features in the absence of similar data.On the one hand, the two-way mapping is used to find the internal relationship between the source and target domain data,while the two-way adversary is used to constrain the source and target domain features, realizing the alignment of feature distributions.On the other hand, the CORAL loss function is introduced to minimize the distance between the second-order statistical difference between the source and target domain features, so as to solve the insufficient constraint of mapping relationship caused by the low consistency of HSI data structure in different domains.Experiments on three real HSI datasets show that UHDAC can effectively realize the unsupervised classification of target domain HSI with high classification accuracy by using the labeled HSI data in the source domain.

关键词： unsupervised Heterogeneous Domain adaptation CycleGan Hyperspectral image Classification

Online unsupervised Learning Classification of Pedestrian and Vehicle for Video Surveillance

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Chinese Journal of Electronics 2017年第1期26卷 145-151页

作者： HE Yi SANG Nong GAO Changxin HAN Jun School of Automation Huazhong University of Science and Technology Air Force Early Warning Academy

This paper presents an online unsupervised learning classification of pedestrians and vehicles for video surveillance. Different from traditional methods depending on offline training, our method adopts the online label strategy based on temporal and morphological features,which saves time and labor to a large extent. It extract the moving objects with their features from the original video.An online filtering procedure is adopted to label the moving objects according to certain threshold of speed and area feature. The labeled objects are sent into a SVM classifier to generate the pedestrian & vehicle classifier. Experimental results illustrate that our unsupervised learning algorithm is adapted to polymorphism of the pedestrians and diversity of the vehicles with high classification accuracy.

关键词： Online learning unsupervised Objects classification Video surveillance

unsupervised natural image patch learning

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Computational Visual Media 2019年第3期5卷 229-237页

作者： Dov Danon Hadar Averbuch-Elor Ohad Fried Daniel Cohen-Or Tel-Aviv University Tel Aviv 6997801Israel Stanford University StanfordCA 94305USA

A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner,requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images. Clearly, relying on this simple principle, many spatially nearby pairs are outliers. However, as we show, these outliers do not harm the convergence of the metric learning. We show that our unsupervised embedding approach is more effective than a supervised one or one that uses deep patch representations. Moreover, we show that it naturally lends itself to an efficient self-supervised domain adaptation technique onto a target domain that contains a common foreground object.

关键词： unsupervised learning metric learning

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Learning Hand Latent Features for unsupervised 3D Hand Pose Estimation

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Journal of Autonomous Intelligence 2019年第1期2卷 1-10页

作者： Jamal Banzi Isack Bulugu Zhongfu Ye School of Information Science and Technology University of Science and Technology of China230026China Sokoine University of Agriculture Morogoro3167Tanzania College of information and communication Technology University of Dare-es-salaamDar-es-Salaam33335Tanzania

Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation.Nevertheless,precise and dense annotation on the real data is difficult to come by and the amount of information passed to the training algorithm is significantly higher.This paper presents an approach to developing a hand pose estimation system which can accurately regress a 3D pose in an unsupervised manner.The whole process is performed in three stages.Firstly,the hand is modelled by a novel latent tree dependency model (LTDM) which transforms internal joints location to an explicit representation.Secondly,we perform predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision.A mapping is then performed between an image depth and a generated representation.Thirdly,the hand joints are regressed using convolutional neural networks to finally estimate the latent pose given some depth map.Finally,an unsupervised error term which is a part of the recurrent architecture ensures smooth estimation of the final pose.To demonstrate the performance of the proposed system,a complete experiment was conducted on three challenging public datasets,ICVL,MSRA,and NYU.The empirical results show the significant performance of our method which is comparable or better than the state-of-the-art approaches.

关键词： Hand Pose Estimation Convolutional Neural Networks Recurrent Neural Networks Human-machine Interaction Predictive Coding unsupervised Learning

unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings

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International Journal of Communications, Network and System Sciences 2014年第7期7卷 203-211页

作者： Lácides R. Pinto Alejandro R. Zambrano Universidad de La Guajira UNIGUAJIRA Riohacha Colombia Universidad Nacional Experimental Politécnica UNEXPO Puerto Ordaz Venezuela

In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is achieved by training the network. The frame will deform so that all joints will rotate an angle. At the same time, a relative lateral sway will be produced at the rth floor level, assuming that the effects of axial lengths of the bars of the structure are not altered. The issue of choosing an appropriate neural network structure and providing structural parameters to that network for training purposes is addressed by using an unsupervised algorithm. The model’s parameters, as well as the rotational variables, are investigated in order to get the most accurate results. The model is then evaluated by using the iteration method of frame analysis developed by Dr. G. Kani. In general, the new approach delivers better results compared to several commonly used methods of structural analysis.

关键词： Structural Analysis Neural Networks unsupervised Training End Moments Rotational End Moments

unsupervised Feature Transfer for Batch Process Based on Geodesic Flow Kernel

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Unsupervised Feature Transfer for Batch Process Based on Geo...