硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
影响因子：26.7
发表刊物：IEEE Transactions on Pattern Analysis and Machine Intelligence
刊物所在地：UNITED STATES
关键字：Clustering, micro-supervised disturbance learning, representation probability distribution, small-perturbation
摘要：The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on as few labels as possible. To address above issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of Restricted Boltzmann Machine (RBM), namely, Micro-supervised Disturbance Gaussian-binary RBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has no external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering.
合写作者：Jing Liu,Hongjun Wang,Hua Meng,Zhiguo Gong
第一作者：Jielei Chu
论文类型：SCI
通讯作者：Tianrui Li
学科门类：工学
文献类型：J
卷号：45
期号：6
页面范围：7542-7558
ISSN号：0162-8828
是否译文：否
发表时间：2022-11-29
收录刊物：SCI