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第43届中国控制会议

作者： Xuemin Hu Dan Xiong Li Chai School of Information Science and Engineering Wuhan University of Science and Technology State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

Concept factorization-based multi-view clustering methods have received much attention and achieved promising ***,they are limited by uncertain noise in the *** improve the robustness of concept factorization-based multi-view clustering methods,a self-paced learning strategy that considers the potential relationship between the distribution of samples and the training order of samples is *** this paper,we propose a joint self-paced learning and concept factorization(SPLCF) method for multi-view *** is,during the training process,self-paced learning can identify the noise data and adaptively assign different weights to different sample *** addition,to preserve the local manifold structure,the graph regularization term is used to effectively constrain the structural features on the data manifold.l2,12-norm is applied to the association matrix,which aims to retain discriminative features and further alleviate the effect of noise in the ***,an iterative algorithm is presented to solve the optimization *** numerical experiments on four benchmark datasets have demonstrated that the proposed algorithm outperforms other state-of-the-art algorithms.

关键词： self-paced learning Concept factorization Sparsity constraint Multi-view clustering

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Steel surface defects classification method based on self-paced learning

Steel surface defects classification method based on self-pa...

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第三十九届中国控制会议

作者： Delong Liang Dali Chen Shixin Liu Xu Jia Chunna Zhao Northeastern University College of Information Science and Engineering School of Electronics and Information Engineering Liaoning University of Technology School of Information Yunnan University

Steel surface defects are important factors that determine the quality of steel products. Automatic classification of steel surface defects can improve steel utilization and product quality. However, due to the low quality and small amount of steel surface defect data, the conventional classifiers are difficult to obtain a good classification result. self-paced learning（SPL）provides a learning paradigm from "simplify" to "difficulty." We combine the idea of SPL with DenseNet and propose the SPDenseNet classification framework. A special self-paced process is designed for the surface defects of steel. In addition, the relationship between SPL and sample quality is further discussed. Experiments show that SP-DenseNet can solve the problem of classification of steel surface defects well. And for data sets with low sample quality, SP-DenseNet improves the classification results even more.

关键词： self-paced learning steel surface defects deep learning

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DSP-TMM:A Robust Cluster Analysis Method Based on Diversity self-paced T-Mixture Model

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Journal of Beijing Institute of Technology 2020年第4期29卷 531-543页

作者： Limin Pan Xiaonan Qin Senlin Luo Information System and Security&Countermeasures Experimental Center Beijing Institute of TechnologyBeijing 100081China

In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture *** model firstly adopts the t-distribution as the submodel which tail is easily *** this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial *** that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in ***,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering *** provides significant guidance for the construction of the robust mixture distribution model.

关键词： cluster analysis Gaussian mixture model t-distribution mixture model self-paced learning initialization

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