Results 251 to 260 of about 387,320 (324)

Discriminative and Robust Autoencoders for Unsupervised Feature Selection

IEEE Transactions on Neural Networks and Learning Systems, 2023
Many recent research works on unsupervised feature selection (UFS) have focused on how to exploit autoencoders (AEs) to seek informative features. However, existing methods typically employ the squared error to estimate the data reconstruction, which ...
Yunzhi Ling, F. Nie, Weizhong Yu, Xuelong Li   +3 more
semanticscholar   +1 more source

Feature Subspace Learning-Based Binary Differential Evolution Algorithm for Unsupervised Feature Selection

IEEE Transactions on Big Data
It is a challenging task to select the informative features that can maintain the manifold structure in the original feature space. Many unsupervised feature selection methods still suffer the poor cluster performance in the selected feature subset.
Tao Li, Yuhua Qian, Feijiang Li, Xinyan Liang, Zhi-hui Zhan   +4 more
semanticscholar   +1 more source

Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information.
Han Yue, Jundong Li, Hongfu Liu
semanticscholar   +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, Wenwu Zhu
openaire   +1 more source

Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference

International Joint Conference on Artificial Intelligence
As data with diverse representations become high-dimensional, multi-view unsupervised feature selection has been an important learning paradigm. Generally, existing methods encounter the following challenges: (i) traditional solutions either concatenate ...
Chenglong Zhang, Yang Fang, Xinyan Liang, Han Zhang, Peng Zhou, Xingyu Wu, Jie Yang, Bingbing Jiang, Weiguo Sheng   +8 more
semanticscholar   +1 more source

Multiple graph unsupervised feature selection

Signal Processing, 2016
Feature selection improves the quality of the model by filtering out the noisy or redundant part. In the unsupervised scenarios, the selection is challenging due to the unavailability of the labels. To overcome that, the graphs which can unfold the geometry structure on the manifold are usually used to regularize the selection process. These graphs can
Du, Xingzhong, Yan, Yan, Pan, Pingbo, Long, Guodong, Zhao, Lei   +4 more
openaire   +2 more sources

Unsupervised Adaptive Feature Selection With Binary Hashing

IEEE Transactions on Image Processing, 2023
Unsupervised feature selection chooses a subset of discriminative features to reduce feature dimension under the unsupervised learning paradigm. Although lots of efforts have been made so far, existing solutions perform feature selection either without any label guidance or with only single pseudo label guidance.
Dan Shi, Lei Zhu, Jingjing Li, Zheng Zhang, Xiaojun Chang   +4 more
openaire   +2 more sources

Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion

ACM Multimedia
To tackle the high-dimensional data with multiple representations, multi-view unsupervised feature selection has emerged as a significant learning paradigm.
Chenglong Zhang, Xinyan Liang, Peng Zhou, Zhaolong Ling, Yingwei Zhang, Xingyu Wu, Weguo Sheng, Bingbing Jiang   +7 more
semanticscholar   +1 more source

Partition-Level Tensor Learning-Based Multiview Unsupervised Feature Selection

IEEE Transactions on Neural Networks and Learning Systems
Multiview unsupervised feature selection is an emerging direction in the machine learning community because of its ability to identify informative patterns and reduce the dimensionality of multiview data.
Zhiwen Cao, Xijiong Xie
semanticscholar   +1 more source

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
openaire   +1 more source