Tinghua Wang

Gannan Normal University

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i-10 index

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Co-Authors

Yun Yi

Gannan Normal University

Harry Liu

Huaneng Clean Energy Research Institute

Liming Xu

Tianjin Medical University Cancer Institute and Hospital

Yicai Xie

Chongqing University of Posts and Telecommunications

Xianhua Zeng

Chongqing University of Posts and Telecommunications

Dingjie Wang

National University of Defense Technology

Guangquan Zhang

University of Technology Sydney

Daili Zhang

Northwestern Polytechnical University

Xiaolu Dai

Hong Kong Baptist University

Xin Zhang

Gannan Normal University

Cooperative Institutions

Gannan Normal University

Chongqing University of Posts and Telecommunications

Sun Yat-sen University

University of Chinese Academy of Sciences

University of Technology Sydney

Northeastern University

Sichuan University

Tongji University

National University of Defense Technology

Arizona State University

Author Statistics

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Research Field

Learning with Hilbert–Schmidt independence criterion: A review and new perspectives

Knowledge-Based Systems (2021)

Tinghua Wang Xiaolu Dai Yuze Liu

Instance-based learning

Independence

Transfer of learning

10.1016/j.knosys.2021.107567

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Citations (22)

Deep online cross-modal hashing by a co-training mechanism

Knowledge-Based Systems (2022)

Yicai Xie Xianhua Zeng Tinghua Wang Yun Yi Liming Xu

Benchmark (surveying)

Dynamic perfect hashing

Feature hashing

Similarity (geometry)

10.1016/j.knosys.2022.109888

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Citations (8)

Multiple deep neural networks with multiple labels for cross-modal hashing retrieval

Engineering Applications of Artificial Intelligence (2022)

Yicai Xie Xianhua Zeng Tinghua Wang Liming Xu Dingjie Wang

Benchmark (surveying)

Discriminative model

Similarity (geometry)

10.1016/j.engappai.2022.105090

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Citations (11)

Discrete subspace structure constrained human motion capture data recovery

Applied Soft Computing (2022)

Wenyu Hu Xuefang Zhu Tinghua Wang Yun Yi Gaohang Yu

Matrix norm

Rank (graph theory)

Low-rank approximation

Regularization

Motion Capture

Matrix Completion

10.1016/j.asoc.2022.109617

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Citations (4)

Fire Video Recognition Based on Local and Global Adaptive Enhancement

Algorithms (2025)

Jian Ding Yun Yi Tinghua Wang Tian Tao

Fires pose an enormous risk to human life and property. In the domain of fire warning, earlier approaches leveraging computer vision have achieved significant progress. However, these methods ignore the local and global motion characteristics of flames. To address this issue, a Local and Global Adaptive Enhancement (LGAE) network is proposed, which mainly includes the backbone block, the Local Adaptive Motion Enhancement (LAME) block, and the Global Adaptive Motion Enhancement (GAME) block. Specifically, the LAME block is designed to capture information about local motion, and the GAME block is devised to enhance information about global motion. Through the utilization of these two blocks, the fire recognition ability of LGAE is improved. To facilitate the research and development in the domain of fire recognition, we constructed a Large-scale Fire Video Recognition (LFVR) dataset, which includes 11,560 video clips. Extensive experiments were carried out on the LFVR and FireNet datasets. The F1 scores of LGAE on LFVR and FireNet were 88.93% and 93.18%, respectively. The experimental outcomes indicate that LGAE performs better than other methods on both LFVR and FireNet.

10.3390/a18010008

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Citations (0)

Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert–Schmidt Independence Criterion

IEEE Transactions on Fuzzy Systems (2018)

Tinghua Wang Jie Lü Guangquan Zhang

Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks, such as classification, clustering, and dimensionality reduction. In this paper, we develop a novel fuzzy multiple kernel learning model based on the Hilbert-Schmidt independence criterion (HSIC) for classification, which we call HSIC-FMKL. In this model, we first propose an HSIC Lasso-based MKL formulation, which not only has a clear statistical interpretation that minimum redundant kernels with maximum dependence on output labels are found and combined, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Since the traditional support vector machine (SVM) is sensitive to outliers or noises in the dataset, fuzzy SVM (FSVM) is used to select the prediction hypothesis once the optimal kernel has been obtained. The main advantage of FSVM is that we can associate a fuzzy membership with each data point such that these data points can have different effects on the training of the learning machine. We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages. We perform extensive experiments on real-world datasets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed model in terms of prediction accuracy.

Kernel (algebra)

Independence

10.1109/tfuzz.2018.2848224

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Citations (39)

Toward effective SVM sample reduction based on fuzzy membership functions

Chemometrics and Intelligent Laboratory Systems (2024)

Tinghua Wang Daili Zhang Harry Liu

Sample (material)

10.1016/j.chemolab.2024.105233

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Citations (0)

A unified view of feature selection based on Hilbert-Schmidt independence criterion

Chemometrics and Intelligent Laboratory Systems (2023)

Tinghua Wang Zhenwei Hu Harry Liu

Benchmark (surveying)

Lasso

Feature (linguistics)

Independence

Supervised Learning

10.1016/j.chemolab.2023.104807

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Citations (8)

A Feature-Weighted Support Vector Regression Machine Based on Hilbert–Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator

Information (2024)

Xin Zhang Tinghua Wang Zhiyong Lai

Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) for selecting features with minimal redundancy and maximum relevance, we propose a novel feature-weighted SVR that considers the importance of features to the output and the redundancy between features. In this approach, the HSIC is utilized to effectively measure the correlation between features as well as that between features and the output. The feature weights are obtained by solving a LASSO regression problem. Compared to other feature weighting methods, our method takes much more comprehensive consideration of weight calculation, and the obtained weighted kernel function can lead to more precise predictions for unknown data. Comprehensive experiments on real datasets from the University of California Irvine (UCI) machine learning repository demonstrate the effectiveness of the proposed method.

Independence

Shrinkage

Operator (biology)

10.3390/info15100639

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Citations (1)