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Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie and Caixia Zheng
As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature se...
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Rui-Yu Li, Yu Guo and Bin Zhang
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extens...
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Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
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Meng Wu and Pudong Shi
To address the problem of poor detection and under-utilization of the spatial relationship between nodes in human pose estimation, a method based on an improved spatial temporal graph convolutional network (ST-GCN) model is proposed. Firstly, upsampling ...
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Camille Champion, Anne-Claire Brunet, Rémy Burcelin, Jean-Michel Loubes and Laurent Risser
In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularizatio...
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Jindou Zhang and Jing Li
Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain ...
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