Results 251 to 260 of about 53,359 (288)

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
openaire   +3 more sources

Unsupervised feature selection with ensemble learning

Machine Learning, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elghazel, Haytham, Aussem, Alex
openaire   +3 more sources

Adaptive Unsupervised Feature Selection With Structure Regularization

IEEE Transactions on Neural Networks and Learning Systems, 2018
© 2012 IEEE. 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
Minnan Luo, Feiping Nie, Xiaojun Chang   +2 more
exaly   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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.
Huyunting Huang, Ziyang Tang, Tonglin Zhang, Baijian Yang 0001, Qianqian Song, Jing Su 0003   +5 more
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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
openaire   +1 more source

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
openaire   +3 more sources

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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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