Results 271 to 280 of about 387,320 (324)

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, Ming Shao, Yun Fu
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Unsupervised feature selection based on feature relevance

2009 International Conference on Machine Learning and Cybernetics, 2009
Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems.
null Feng Zhang, null Ya-Jun Zhao, null Jun-Fen   +2 more
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Unsupervised Feature Subset Selection

2003
This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some irrelevant features.
Søndberg-Madsen, Nicolaj, Thomsen, C., Pena, Jose   +2 more
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Unsupervised Feature Selection via Adaptive Multimeasure Fusion

IEEE Transactions on Neural Networks and Learning Systems, 2019
Since multiple criteria can be adopted to estimate the similarity among the given data points, problem regarding diverse representations of pairwise relations is brought about. To address this issue, a novel self-adaptive multimeasure (SAMM) fusion problem is proposed, such that different measure functions can be adaptively merged into a unified ...
Rui Zhang, Feiping Nie, Yunhai Wang, Xuelong Li   +3 more
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Unsupervised feature selection in cardiac arrhythmias analysis

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
The problem of detecting clinical events related to cardiac arrhythmias in long term electrocardiograms is a difficult one due to the large amount of irrelevant information that hides such events. This problem has been addressed in the literature by means of clustering or classification algorithms that create data partitions according to a cost ...
J L, Rodriguez-Sotelo, D, Cuesta-Frau, D, Peluffo-Ordonez, G, Castellanos-Dominguez   +3 more
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Unsupervised feature selection via multiple graph fusion and feature weight learning

Science China Information Sciences, 2023
Chang Tang, Xiao Zheng, Wei Zhang, Xinwang Liu, Xinzhong Zhu, En Zhu   +5 more
semanticscholar   +1 more source

Adaptive Unsupervised Feature Selection With Structure Regularization

IEEE Transactions on Neural Networks and Learning Systems, 2018
Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem.
Minnan Luo, Feiping Nie, Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Qinghua Zheng   +5 more
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Feature Selection for Unsupervised Learning

2012
In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor
Jyoti Ranjan Adhikary, M. Narasimha Murty   +1 more
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Feature Selection Meets Unsupervised Learning

2020
Feature selection is a fundamental problem in learning. We are immersed in a huge quantity of spatial and temporal data, and one of the crucial questions if we want to learn efficiently is to find the key cues that are correlated with our specific learning task.
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Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering

Jordanian Journal of Informatics and Computing
Text clustering is suitable for dividing many text documents into distinct groups. The size of the documents has an impact on the performance of text clustering, reducing its effectiveness.
Mohammad Alshinwan, Abdul Ghafoor Memon, Mohamed Chahine Ghanem, Mohammed Almaayah   +3 more
semanticscholar   +1 more source