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Symmetry
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Asymmetry in Machine Learning
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Asymmetry in Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1003

Special Issue Editors

Dr. Hui Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 210018, China
Interests: machine vision; intelligent manufacturing; mechanical equipment quality inspection and software robot
Prof. Dr. Kuo-Hui Yeh

E-Mail Website
Guest Editor
Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
Interests: IoT security; blockchain; mobile security; NFC/RFID security; authentication; data privacy; cryptology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning enables machines to learn automatically without explicit programming. The main process is to use advanced algorithms and statistical techniques to access the data and predict accuracy, instead of using a rule-based system. There are many well-established algorithms for prediction and analysis, such as supervised learning. Machine learning algorithms include support vector machine (SVM), KNN, YOLO, etc. Scipy, Scikit, OpenCV, Matplotlib, and Keras are popular libraries used for image segmentation. The dataset is a primary component of machine learning accuracy prediction. As a result, the data are more relevant, and the prediction is more accurate. Machine learning has been used in different fields, such as finance, retail, and the healthcare industry. Especially, the increasing use of machine learning in healthcare provides more opportunities for disease diagnosis and treatment. Machine learning continually improves, enalbing more accurate data prediction and classification for analysis. The prediction model will learn to make a better decisions for accurate prediction, as more data are gathered. Asymmetry in machine learning has recently demonstrated outstanding results in the fields of engineering, health, agriculture, astronomy, sports, cyber security, and education. This Special Issue mainly focuses on novel machine learning models motivated by symmetry/asymmetry.

Dr. Hui Zhang
Prof. Dr. Kuo-Hui Yeh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • prediction algorithms
  • accuracy
  • SVM
  • data set

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Published Papers (1 paper)

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Research

17 pages, 2642 KiB  
Article
Exploiting Semantic-Visual Symmetry for Goal-Oriented Zero-Shot Recognition
by Haixia Zheng, Yu Zhou and Mingjie Jiang
Symmetry 2025, 17(8), 1291; https://doi.org/10.3390/sym17081291 - 11 Aug 2025
Viewed by 45
Abstract
Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task, zero-shot learning is introduced, which incorporates [...] Read more.
Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task, zero-shot learning is introduced, which incorporates additional semantic information to establish a semantic-visual symmetry, thereby facilitating the transfer of knowledge from known to unknown classes. Although user-defined attributes are commonly utilized to provide prior semantic information for zero-shot recognition, their importance for discrimination is not always consistent. Motivated by the observation that there exist both latent discriminative features and attributes in the images, this paper proposes a goal-oriented joint learning architecture to establish the symmetric relationships between images, attributes and categories for zero-shot learning. To be more specific, we model the latent feature and attribute spaces using the dictionary learning architecture. To learn the symmetric relationships between latent features and latent attributes, a linear transformation is applied while maintaining the semantic information. Moreover, seen-class classifiers are trained to enhance the discriminability of latent features. Extensive experiments on three representative benchmark datasets show that the proposed algorithm outperforms existing methods, highlighting the effectiveness of modeling explicit symmetry in the semantic-visual space for robust zero-shot knowledge transfer. Full article
(This article belongs to the Special Issue Asymmetry in Machine Learning)
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