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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 +4 more
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A review of unsupervised feature selection methods
Artificial Intelligence Review, 2019In 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 ...
Saul Solorio-Fernández +2 more
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Unsupervised soft-label feature selection
Knowledge-Based Systems, 2021Abstract 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.
Lei Zhu, Jingjing Li, Huaxiang Zhang
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An unsupervised attribute clustering algorithm for unsupervised feature selection
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015 The curse of dimensionality refers to the problem that one faces when analyzing datasets with thousands or hundreds of thousands of attributes. This problem is usually tackled by different feature selection methods which have been shown to effectively reduce computation time, improve prediction performance, and facilitate better understanding of ...
Pei-Yuan Zhou, Keith C. C. Chan
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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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Dependence Guided Unsupervised Feature Selection
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 In the past decade, various sparse learning based unsupervised feature selection methods have been developed. However, most existing studies adopt a two-step strategy, i.e., selecting the top-m features according to a calculated descending order and then performing K-means clustering, resulting in a group of sub-optimal features.
Jun Guo 0008, Wenwu Zhu 0001
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Consensus Guided Unsupervised Feature Selection
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Feature selection has been widely recognized as one of the key problems in data mining and machine learning community, especially for high-dimensional data with redundant information, partial noises and outliers. Recently, unsupervised feature selection attracts substantial research attentions since data acquisition is rather cheap ...
Hongfu Liu 0001, Ming Shao, Yun Fu 0001
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Selective Deep Autoencoder for Unsupervised Feature Selection
Proceedings of the AAAI Conference on Artificial IntelligenceIn 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 A. Chehade
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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 ...
Dan Shi, Lei Zhu, Jingjing Li
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Unsupervised feature selection by regularized self-representation
Pattern Recognition, 2015 By removing the irrelevant and redundant features, feature selection aims to find a compact representation of the original feature with good generalization ability.
Pengfei Zhu, Wangmeng Zuo, Qinghua Hu
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