Results 261 to 270 of about 387,320 (324)

A review of unsupervised feature selection methods

Artificial Intelligence Review, 2019
Saúl Solorio-Fernández, Jesus Ariel Carrasco-Ochoa   +1 more
exaly   +2 more sources

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
openaire   +1 more source

Two-Dimensional Unsupervised Feature Selection via Sparse Feature Filter

IEEE Transactions on Cybernetics, 2023
Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D feature selection methods show good performance on image analysis by utilizing the structure information of image. Current 2-D methods usually adopt a sparse regularization to spotlight the key features.
Junyu Li, Jiazhou Chen, Fei Qi, Tingting Dan, Wanlin Weng, Bin Zhang, Haoliang Yuan, Hongmin Cai, Cheng Zhong   +8 more
openaire   +2 more sources

Unsupervised Feature Selection via Collaborative Embedding Learning

IEEE Transactions on Emerging Topics in Computational Intelligence
Unsupervised feature selection is vital in explanatory learning and remains challenging due to the difficulty of formulating a learnable model. Recently, graph embedding learning has gained widespread popularity in unsupervised learning, which extracts ...
Junyu Li, Fei Qi, Xin Sun, Bin Zhang, Xiangmin Xu, Hongmin Cai   +5 more
semanticscholar   +1 more source

Unsupervised neuro-fuzzy feature selection

1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), 2002
This article describes a neuro-fuzzy methodology for feature selection under unsupervised training. The methodology includes connectionist minimization of a fuzzy feature evaluation index. A concept of flexible membership function incorporating weighted distance is introduced in the evaluation index to make the modeling of clusters more appropriate.
J. Basak, R.K. De, S.K. Pal
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, Qian Xu, Qinghua Hu, Changqing Zhang   +3 more
openaire   +1 more source

Unsupervised feature selection with evolutionary sparsity

Neural Networks
The ℓ2,0-norm is playing an increasingly important role in unsupervised feature selection. However, existing algorithm for optimization problem with ℓ2,0-norm constraint has two problems: First, they cannot automatically determine the sparsity, also known as the number of key features.
Shixuan Zhou, Yi Xiang, Han Huang, Pei Huang, Chaoda Peng, Xiaowei Yang, Peng Song   +6 more
openaire   +2 more sources

Unsupervised Feature Selection Using RST

2017
Supervised feature selection evaluates the features that provide maximum information based on classification accuracy. This requires labelled data; however, in real world not all the data is properly labelled, so we may come across the situation where little or no class information is provided.
Muhammad Summair Raza, Usman Qamar
openaire   +1 more source

Structure preserving unsupervised feature selection

Neurocomputing, 2018
Abstract Spectral analysis was usually used to guide unsupervised feature selection. However, the performances of these methods are not always satisfactory due to that they may generate continuous pseudo labels to approximate the discrete real labels.
Lu, Quanmao, Li, Xuelong, Dong, Yongsheng, Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China.   +3 more
openaire   +2 more sources

Unsupervised Feature Ranking and Selection

2005
Dimensionality reduction is an important issue for efficient handling of large data sets. Feature selection is effective in dimensionality reduction. Many supervised feature selection methods exist. Little work has been done for unsupervised feature ranking and selection where class information is not available.
Manoranjan Dash, Huan Liu, Jun Yao
openaire   +1 more source