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Selective Deep Autoencoder for Unsupervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence
In light of the advances in big data, high-dimensional datasets are often encountered. Incorporating them into data-driven models can enhance performance; however, this comes at the cost of high computation and the risk of overfitting, particularly due to abundant redundant features.
Wael Hassanieh, Abdallah Chehade
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Feature Selection for Unsupervised Machine Learning

2023 IEEE 8th International Conference on Smart Cloud (SmartCloud), 2023
Compared to supervised machine learning (ML), the development of feature selection for unsupervised ML is far behind. To address this issue, the current research proposes a stepwise feature selection approach for clustering methods with a specification to the Gaussian mixture model (GMM) and the k-means.
Huang, Huyunting, Tang, Ziyang, Zhang, Tonglin, Yang, Baijian, Ph.D., Song, Qianqian, Su, Jing   +5 more
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Cross-View Locality Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

IEEE Transactions on Knowledge and Data Engineering, 2022
Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view
Chang Tang, Xiao Zheng, Xinwang Liu, Wei Zhang, Jing Zhang, Jian Xiong, Lizhe Wang   +6 more
semanticscholar   +1 more source

A Possibilistic Information Fusion-Based Unsupervised Feature Selection Method Using Information Quality Measures

IEEE transactions on fuzzy systems, 2023
The main goal of most information quality (IQ)-based measures is to combine data provided by multiple information sources to enhance the quality of information essential for decision makers to perform their tasks.
Pengfei Zhang, Tianrui Li, Zhong Yuan, Zhixuan Deng, Guoqiang Wang, Dexian Wang, Fanfan Zhang   +6 more
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Robust Unsupervised Feature Selection via Multi-Group Adaptive Graph Representation

IEEE Transactions on Knowledge and Data Engineering, 2023
Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method,
Mengbo You, Aihong Yuan, Min Zou, Dongjian He, Xuelong Li   +4 more
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Fast Sparse Discriminative K-Means for Unsupervised Feature Selection

IEEE Transactions on Neural Networks and Learning Systems, 2023
Embedded feature selection approach guides subsequent projection matrix (selection matrix) learning through the acquisition of pseudolabel matrix to conduct feature selection tasks. Yet the continuous pseudolabel matrix learned from relaxed problem based
F. Nie, Zhenyu Ma, Jingyu Wang, Xuelong Li   +3 more
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Graph-Based Unsupervised Feature Selection for Interval-Valued Information System

IEEE Transactions on Neural Networks and Learning Systems, 2023
Feature selection has become one of the hot research topics in the era of big data. At the same time, as an extension of single-valued data, interval-valued data with its inherent uncertainty tend to be more applicable than single-valued data in some ...
Weihua Xu, Man Huang, Zongying Jiang, Yuhua Qian   +3 more
semanticscholar   +1 more source

Scalable and Flexible Unsupervised Feature Selection

Neural Computation, 2019
Recently, graph-based unsupervised feature selection algorithms (GUFS) have been shown to efficiently handle prevalent high-dimensional unlabeled data. One common drawback associated with existing graph-based approaches is that they tend to be time-consuming and in need of large storage, especially when faced with the increasing size of data. Research
Hu, Haojie, Wang, Rong, Yang, Xiaojun, Nie, Feiping   +3 more
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Unsupervised soft-label feature selection

Knowledge-Based Systems, 2021
Abstract Unsupervised feature selection is an important task in various research fields. It is difficult to select the discriminative features under unsupervised scenario due to the absence of label guidance. Recent works employ the pseudo labels to guide feature selection.
Fei Wang, Lei Zhu, Jingjing Li, Haibao Chen, Huaxiang Zhang   +4 more
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Rethinking Embedded Unsupervised Feature Selection: A Simple Joint Approach

IEEE Transactions on Big Data, 2023
Recently, various embedded methods for unsupervised feature selection have been put forward. However, most of them adopt a two-step strategy, i.e., selecting $k$k top-ranked dimensions according to a learned order of all features, then conducting K-means
Heng Chang, J. Guo, Wenwu Zhu
semanticscholar   +1 more source