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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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Pairwise Constraints Multidimensional Scaling for Discriminative Feature Learning
DOI码：10.3390/math10214059
所属单位：西南交通大学
发表刊物：Mathematics
刊物所在地：SWITZERLAND
关键字：discriminative feature learning; multidimensional scaling; fuzzy k-means; pairwise constraint propagation; iterative majorization algorithm
摘要：As an important data analysis method in the field of machine learning and data mining, feature learning has a wide range of applications in various industries. The traditional multidimensional scaling (MDS) maintains the topology of data points in the low-dimensional embeddings obtained during feature learning, but ignores the discriminative nature between classes of low-dimensional embedded data. Thus, the discriminative multidimensional scaling based on pairwise constraints for feature learning (pcDMDS) model is proposed in this paper. The model enhances the discriminativeness from two aspects. The first aspect is to increase the compactness of the new data representation in the same cluster through fuzzy k-means. The second aspect is to obtain more extended pairwise constraint information between samples. In the whole feature learning process, the model considers both the topology of samples in the original space and the cluster structure in the new space. It also incorporates the extended pairwise constraint information in the samples, which further improves the model’s ability to obtain discriminative features. Finally, the experimental results on twelve datasets show that pcDMDS performs
合写作者：Bo Pang,Haitao Tang,Chongshou Li,Zhipeng Luo
第一作者：Linghao Zhang
论文类型：SCI
通讯作者：Hongjun Wang
学科门类：工学
文献类型：J
卷号：10
期号：21
页面范围：4059
ISSN号：2227-7390
是否译文：否
发表时间：2022-11-01
收录刊物：SCI