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Advancement of Mathematical Methods in Feature Representation Learning for Artificial...
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Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4976

Special Issue Editors

Prof. Dr. Jianping Gou

E-Mail Website
Guest Editor
College of Computer and Information Science, Southwest University, Chongqing 400700, China
Interests: model compression; feature representation learning; deep dictionary learning; graph embedding; visual recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weihua Ou

E-Mail Website
Guest Editor
School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550001, China
Interests: cross media analysis; computer vision; camera-based vital sign measurement; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shaoning Zeng

E-Mail Website
Guest Editor
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 314006, China
Interests: multi-media analysis; computer vIsion; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lan Du

E-Mail Website
Guest Editor
Faculty of Information Technology, Monash University, Melbourne 3800, Australia
Interests: machine learning; natural language processing; public health; healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current Special Issue is devoted to the advancement of mathematical methods in artificial intelligence, data mining, and robotics. Big data have boosted the rapid development of new techniques in artificial intelligence (AI), data mining, and robotics in the past decade. However, this development has been subject to the mathematical foundation under feature representation learning in the developed models, especially the ones based on deep neural networks. Due to this, the efficiency, reliability, and security of AI models are likely to be influenced. The topic of this Special Issue covers a wide range of algorithms, methods, and applications of explainable representation learning from a mathematical perspective. Topics of interest include but are not limited to:

  1. Visual recognition methods and algorithms;
  2. Explainable deep learning and its applications;
  3. Theory of representation learning;
  4. Data mining approaches;
  5. Model compression;
  6. Deep dictionary learning;
  7. Knowledge discovery systems;
  8. Human-based computer vision.

Prof. Dr. Jianping Gou
Prof. Dr. Weihua Ou
Dr. Shaoning Zeng
Dr. Lan Du
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • visual recognition
  • deep learning
  • knowledge distillation
  • representation learning
  • data mining

Published Papers (4 papers)

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Research

13 pages, 659 KiB  
Article
A Semantic Enhancement Framework for Multimodal Sarcasm Detection
by Weiyu Zhong, Zhengxuan Zhang, Qiaofeng Wu, Yun Xue and Qianhua Cai
Mathematics 2024, 12(2), 317; https://doi.org/10.3390/math12020317 - 18 Jan 2024
Viewed by 745
Abstract
Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus [...] Read more.
Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus on modeling text–image incongruity at the token level and use the incongruity as the key to detection, ignoring the significance of the overall multimodal features and textual semantics during processing. Moreover, semantic information from other samples with a similar manner of expression also facilitates sarcasm detection. In this work, a semantic enhancement framework is proposed to address image–text congruity by modeling textual and visual information at the multi-scale and multi-span token level. The efficacy of textual semantics in multimodal sarcasm detection is pronounced. Aiming to bridge the cross-modal semantic gap, semantic enhancement is performed by using a multiple contrastive learning strategy. Experiments were conducted on a benchmark dataset. Our model outperforms the latest baseline by 1.87% in terms of the F1-score and 1% in terms of accuracy. Full article
(This article belongs to the Special Issue Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2nd Edition)
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17 pages, 1145 KiB  
Article
Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification
by Jiehai Chen, Zhixun Qiu, Junxi Liu, Yun Xue and Qianhua Cai
Mathematics 2023, 11(18), 3877; https://doi.org/10.3390/math11183877 - 11 Sep 2023
Viewed by 628
Abstract
Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax [...] Read more.
Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model. Full article
(This article belongs to the Special Issue Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2nd Edition)
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16 pages, 2154 KiB  
Article
Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews
by Dehong Zeng, Xiaosong Chen, Zhengxin Song, Yun Xue and Qianhua Cai
Mathematics 2023, 11(10), 2335; https://doi.org/10.3390/math11102335 - 17 May 2023
Cited by 3 | Viewed by 1143
Abstract
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network [...] Read more.
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network to deal with both texts and images on the task of document-level multimodal sentiment analysis. The image caption is introduced as an auxiliary, which is aligned with the image to enhance the semantics delivery. Then, a graph is constructed with the sentences and images generated as nodes. In line with the graph learning, the long-distance dependencies can be captured while the visual noise can be filtered. Specifically, a cross-modal graph convolutional network is built for multimodal information fusion. Extensive experiments are conducted on a multimodal dataset from Yelp. Experimental results reveal that our model obtains a satisfying working performance in DLMSA tasks. Full article
(This article belongs to the Special Issue Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2nd Edition)
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18 pages, 1436 KiB  
Article
Robust Graph Structure Learning with Virtual Nodes Construction
by Wenchuan Zhang, Weihua Ou, Weian Li, Jianping Gou, Wenjun Xiao and Bin Liu
Mathematics 2023, 11(6), 1397; https://doi.org/10.3390/math11061397 - 13 Mar 2023
Viewed by 1606
Abstract
Graph neural networks (GNNs) have garnered significant attention for their ability to effectively process graph-related data. Most existing methods assume that the input graph is noise-free; however, this assumption is frequently violated in real-world scenarios, resulting in impaired graph representations. To address this [...] Read more.
Graph neural networks (GNNs) have garnered significant attention for their ability to effectively process graph-related data. Most existing methods assume that the input graph is noise-free; however, this assumption is frequently violated in real-world scenarios, resulting in impaired graph representations. To address this issue, we start from the essence of graph structure learning, considering edge discovery and removal, reweighting of existing edges, and differentiability of the graph structure. We introduce virtual nodes and consider connections with virtual nodes to generate optimized graph structures, and subsequently utilize Gumbel-Softmax to reweight edges and achieve differentiability of the Graph Structure Learning (VN-GSL for abbreviation). We conducted a thorough evaluation of our method on a range of benchmark datasets under both clean and adversarial circumstances. The results of our experiments demonstrate that our approach exhibits superiority in terms of both performance and efficiency. Our implementation will be made available to the public. Full article
(This article belongs to the Special Issue Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2nd Edition)
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