Results 241 to 250 of about 3,745,728 (280)

Unsupervised feature selection for balanced clustering

Knowledge-Based Systems, 2020
Abstract In many real-world applications of data mining, such as energy load balance of wireless sensor networks, given data points with balanced distribution, i.e., each class contains approximately the same number of instances, we often need to obtain a clustering result to reflect such balance.
Peng Zhou 0006, Jiangyong Chen, Mingyu Fan, Liang Du 0003, Yi-Dong Shen, Xuejun Li 0001   +5 more
openaire   +2 more sources

Unsupervised feature selection for attributed graphs

Expert Systems with Applications, 2021
Abstract Many real-world applications generate attributed graphs that contain both link structures and content information associated with nodes. Content information in real networks always contains high dimensional feature space. In recent years, unsupervised feature selection has been widely used in handling high dimensional data without label ...
Ruizhi Zhou, Lingfeng Niu, Hong Yang 0003   +2 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

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
openaire   +1 more source

Co-regularized unsupervised feature selection

Neurocomputing, 2018
Abstract Unsupervised feature selection (UFS) is very challenging due to the lack of label information. Most UFS methods generate pseudo labels by spectral clustering, matrix factorization or dictionary learning, and convert UFS into a supervised feature selection problem. Generally, the features that can preserve the data distribution (i.e., cluster
Pengfei Zhu 0001, Qian Xu, Qinghua Hu, Changqing Zhang 0002   +3 more
openaire   +1 more source

Unsupervised feature selection with ordinal locality

2017 IEEE International Conference on Multimedia and Expo (ICME), 2017
Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks.
Jun Guo 0008, Yanqing Guo, Xiangwei Kong 0001, Ran He 0001   +3 more
openaire   +2 more sources

Unsupervised Feature Selection for Ensemble of Classifiers

Ninth International Workshop on Frontiers in Handwriting Recognition, 2004
In this paper we discuss a strategy to create ensemble of classifiers based on unsupervised features selection. It takes into account a hierarchical multi-objective genetic algorithm that generates a set of classifiers by performing feature selection and then combines them to provide a set of powerful ensembles.
Marisa E. Morita, Luiz S. Oliveira, Robert Sabourin   +2 more
openaire   +2 more sources

An efficient framework for unsupervised feature selection

Neurocomputing, 2019
Abstract In these years, the task of fast unsupervised feature selection attracts much attentions with the increasing number of data collected from the physical world. To speed up the running time of algorithms, the bipartite graph theory has been applied in many large-scale tasks, including fast clustering, fast feature extraction, etc.
Han Zhang 0012, Rui Zhang 0017, Feiping Nie 0001, Xuelong Li 0001   +3 more
openaire   +2 more sources

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
openaire   +4 more sources

Selective Deep Autoencoder for Unsupervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence
In 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
openaire   +2 more sources