AFFS: Adaptive Fast Frequency Selection Algorithm for Deep Learning Feature Extraction

doi:10.1109/TKDE.2025.3638836

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	AFFS: Adaptive Fast Frequency Selection Algorithm for Deep Learning Feature Extraction
	Li, Xiaocan 1,2; Xie, Kun 1,2; He, Zilong 1,2; Wen, Jigang 3; Cao, Jiannong 4; Zhang, Guangxing 5; Xie, Gaogang 6; Liang, Wei 3
	2026-02-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	38 期号:2 页码:842-856
摘要	As deep learning (DL) continues to advance, effective feature extraction from large-scale data remains crucial for enhancing model performance. To leverage the advantages of the frequency domain, such as concentrated signal energy, prominent data features, and rich detailed characteristics, this paper proposes a novel frequency-domain feature extraction method. However, existing frequency component selection algorithms often struggle to adapt to diverse tasks, tend to yield only locally optimal solutions, and require prolonged processing times. To overcome these limitations, we introduce the Adaptive Fast Frequency Selection (AFFS) algorithm, which seamlessly integrates a frequency component selection factor layer into DL models to identify globally optimal frequency combinations suited to various downstream tasks. We further analyze the relationship between selected frequency components and model performance, providing theoretical guarantees regarding optimality, robustness, and generalization error bounds. Moreover, a fast selection procedure is developed to exploit the empirically observed rapid convergence of the selection-factor ranking, significantly accelerating the selection process. Extensive experiments on five datasets, ten DL models, and two subsequent tasks demonstrate that AFFS achieves superior performance: even when the input data size is reduced to only 10% of the original frequency features, model classification accuracy improves by approximately 1%, while the early stopping mechanism shortens the selection process by about 80%.
关键词	Feature extraction Discrete cosine transforms Adaptation models Training Data models Computational modeling Time-frequency analysis Deep learning Logic gates Image classification Discrete cosine transform feature extraction frequency components selection frequency domain
DOI	10.1109/TKDE.2025.3638836
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001655693200021
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42818
专题	中国科学院计算技术研究所
通讯作者	Xie, Kun
作者单位	1.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410006, Peoples R China 2.Hunan Univ, Key Lab Fus Comp Supercomp & Artificial Intelligen, Minist Educ, Changsha 410082, Peoples R China 3.Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Peoples R China 4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Dept Network Technol Res Ctr, Beijing 100864, Peoples R China 6.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaocan,Xie, Kun,He, Zilong,et al. AFFS: Adaptive Fast Frequency Selection Algorithm for Deep Learning Feature Extraction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2026,38(2):842-856.
APA	Li, Xiaocan.,Xie, Kun.,He, Zilong.,Wen, Jigang.,Cao, Jiannong.,...&Liang, Wei.(2026).AFFS: Adaptive Fast Frequency Selection Algorithm for Deep Learning Feature Extraction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,38(2),842-856.
MLA	Li, Xiaocan,et al."AFFS: Adaptive Fast Frequency Selection Algorithm for Deep Learning Feature Extraction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 38.2(2026):842-856.