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Embedded Unsupervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Sparse learning has been proven to be a powerful techniquein supervised feature selection, which allows toembed feature selection into the classification (or regression)problem. In recent years, increasing attentionhas been on applying spare learning in unsupervisedfeature selection.
Suhang Wang, Jiliang Tang, Huan Liu 0001
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A review of unsupervised feature selection methods

Artificial Intelligence Review, 2019
In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature ...
José Francisco Martínez-Trinidad, Saul Solorio-Fernández, J Ariel Carrasco-Ochoa   +2 more
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Unsupervised Personalized Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Feature selection is effective in preparing high-dimensional data for a variety of learning tasks such as classification, clustering and anomaly detection. A vast majority of existing feature selection methods assume that all instances share some common patterns manifested in a subset of shared features.
Jundong Li, Liang Wu 0006, Harsh Dani, Huan Liu 0001   +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.
Huaxiang Zhang, Lei Zhu, Jingjing Li
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Unsupervised robust Bayesian feature selection

2014 International Joint Conference on Neural Networks (IJCNN), 2014
In this paper, we proposed a generative graphical model for unsupervised robust feature selection. The model assumes that the data are independent and identically sampled from a finite mixture of Student-t distribution for dealing with outliers. The Student t-distribution works as the building block for robust clustering and outlier detection.
Jianyong Sun, Aimin Zhou
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Multiple graph unsupervised feature selection

Signal Processing, 2016
Feature selection improves the quality of the model by filtering out the noisy or redundant part. In the unsupervised scenarios, the selection is challenging due to the unavailability of the labels. To overcome that, the graphs which can unfold the geometry structure on the manifold are usually used to regularize the selection process. These graphs can
Xingzhong Du, Yan Yan 0006, Pingbo Pan, Guodong Long, Lei Zhao 0001   +4 more
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Unsupervised Adaptive Feature Selection With Binary Hashing [PDF]

IEEE Transactions on Image Processing, 2023
Unsupervised feature selection chooses a subset of discriminative features to reduce feature dimension under the unsupervised learning paradigm. Although lots of efforts have been made so far, existing solutions perform feature selection either without ...
Lei Zhu, Jingjing Li, Xiaojun Chang
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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
Haojie Hu, Rong Wang 0001, Xiaojun Yang, Feiping Nie 0001   +3 more
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Unsupervised Discriminative Projection for Feature Selection

IEEE Transactions on Knowledge and Data Engineering, 2022
Feature selection is one of the most important techniques to deal with the high-dimensional data for a variety of machine learning and data mining tasks, such clustering, classification, and retrieval, etc. Fuzziness is a widespread nature of data in nature human society.
Rong Wang 0001, Jintang Bian, Feiping Nie 0001, Xuelong Li 0001   +3 more
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Unsupervised feature selection with ensemble learning

Machine Learning, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elghazel, Haytham, Aussem, Alex
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