Bi-level spectral feature selection
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-INST electrical electronics engineers
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform only at the feature level, i.e., they directly select useful features by feature ranking. Such methods do not pay any attention to the interaction information with other tasks such as classification, which severely degrades their feature selection performance. In this article, we propose an UFS method which also takes into account the classification level, and selects features that perform well both in clustering and classification. To achieve this, we design a bi-level spectral feature selection (BLSFS) method, which combines classification level and feature level. More concretely, at the classification level, we first apply the spectral clustering to generate pseudolabels, and then train a linear classifier to obtain the optimal regression matrix. At the feature level, we select useful features via maintaining the intrinsic structure of data in the embedding space with the learned regression matrix from the classification level, which in turn guides classifier training. We utilize a balancing parameter to seamlessly bridge the classification and feature levels together to construct a unified framework. A series of experiments on 12 benchmark datasets are carried out to demonstrate the superiority of BLSFS in both clustering and classification performance.
Açıklama
Anahtar Kelimeler
Feature Extraction, Task Analysis, Petroleum, Clustering Algorithms Classification Algorithms, Optimization, Linear Programming, Bi-level spectral feature selection (BLSFS), Classification Level, Feature Level, High-Dimensional Data, Unsupervised Learning
Kaynak
IEEE transactions on neural networks and learning systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Hu, Z., Wang, J., Zhang, K., Pedrycz, W., & Pal, N. R. (2024). Bi-Level Spectral Feature Selection. IEEE Transactions on Neural Networks and Learning Systems.
