Neural Networks for Feature Extraction
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 January 2025 | Viewed by 653
Special Issue Editors
Interests: artificial intelligence
Special Issue Information
Dear Colleagues,
Neural networks are powerful machine learning algorithms that can automatically learn feature representations of input data. Compared with traditional methods, using neural networks for feature extraction has the following advantages: as an unsupervised feature learning method, neural networks can automatically discover features in the dataset without manual feature engineering. Deep neural networks can learn more abstract and advanced features of the data, containing information about the attributes and structure of the data, while the features learned by traditional methods tend to be more-superficial and harder-to-learn, abstract features. The features learned by neural networks can be well generalized to new data, while the features learned by traditional methods tend to be too dependent on training data. Neural networks can learn the intrinsic dependencies between features, and such feature representations are often more powerful than simply stacking features.
Neural networks have been widely used in feature extraction tasks, and current research focuses on the following aspects: improving the effectiveness of feature learning, e.g., through regularization and pre-training; and designing new neural network structures to learn more abstract and efficient features, e.g., convolutional neural networks, recurrent neural networks, and spike neural networks.
Combining neural networks with other methods can be carried out to form a more powerful feature learning framework.
In summary, neural networks have the advantages of automatically learning features, learning abstract features, good feature generalization, and convergence to stable features, which often make the features learned by neural networks more powerful than artificial features and widely applicable to downstream tasks. Neural networks provide a powerful tool for feature learning and representation.
Dr. Zhen Cao
Dr. Zhang Guo
Guest Editors
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