Results 251 to 260 of about 3,745,728 (280)

Clustering Ensemble for Unsupervised Feature Selection

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
A new feature selection algorithm for unsupervised learning is proposed. It is based on the assumption that, in absence of class labels, the clustering ensemble result can be employed as a heuristic to guide the feature selection. Therefore?a modified RReliefF algorithm is then used to assign the rankings for every feature.
Yihui Luo, Shuchu Xiong
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An unsupervised approach to feature discretization and selection

Pattern Recognition, 2012
Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene ...
J. Ferreira, Artur, Figueiredo, Mário A. T.   +1 more
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Unsupervised feature selection based on clustering

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010
Feature selection plays an important part in improving the classification accuracy and the quality of clustering in many applications. Feature selection has been widely studied in supervised learning, but in unsupervised learning it is still relatively rare.
Shengyi Jiang, Lianxi Wang 0001
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Unsupervised Joint Feature Discretization and Selection

2011
In many applications, we deal with high dimensional datasets with different types of data. For instance, in text classification and information retrieval problems, we have large collections of documents. Each text is usually represented by a bag-of-words or similar representation, with a large number of features (terms).
Artur J. Ferreira, Mário A. T. Figueiredo   +1 more
openaire   +1 more source

Feature Selection with Unsupervised Consensus Guidance

IEEE Transactions on Knowledge and Data Engineering, 2019
Most of the unsupervised feature selection methods employ pseudo labels generated by clustering to guide the feature selection; however, noisy and irrelevant features degrade the cluster structure, which is ineffective to supervise feature selection.
Hongfu Liu 0001, Ming Shao, Yun Fu 0001
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Discriminant Analysis for Unsupervised Feature Selection

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised ...
Jiliang Tang, Xia Hu 0001, Huiji Gao, Huan Liu 0001   +3 more
openaire   +1 more source

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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Discriminative embedded unsupervised feature selection

Pattern Recognition Letters, 2018
Abstract Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features are selected out for effective data representation. In this paper, we propose a novel unsupervised feature selection method which discovers and exploits the global information of the data by maximizing distances between samples ...
Qi-Hai Zhu, Yu-Bin Yang
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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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Exploring autoencoders for unsupervised feature selection

2015 International Joint Conference on Neural Networks (IJCNN), 2015
Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity ...
B. Chandra 0001, Rajesh Kumar Sharma 0002   +1 more
openaire   +1 more source