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- Zhong JinSuggested from co-authorship
- Jian Jun YangSuggested from co-authorship
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Works316 from public data
- Wavelet Integrated CNNs for Noise-Robust Image Classification258
WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.
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- Joint sparse principal component analysis203
A novel approach called joint sparse principal component analysis (JSPCA) is proposed to jointly select useful features and enhance robustness to outliers and the experimental results demonstrate that the proposed approach is feasible and effective.
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- Low-Rank Preserving Projections157
This paper proposes a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification, and shows the effectiveness and the feasibility of the proposed method with encouraging results.
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- Integrating Conventional and Inverse Representation for Face Recognition149
The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC) can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods.
- Using the idea of the sparse representation to perform coarse-to-fine face recognition145
A coarse-to-fine face recognition method that identifies the classes that are ''far'' from the test sample and removes them from the set of the training samples and the classification problem becomes a simpler one with fewer classes.
- Structured optimal graph based sparse feature extraction for semi-supervised learning143
A novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed, in which the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction.
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- Regularized Label Relaxation Linear Regression121
A novel regularized label relaxation LR method is proposed, which relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible.
- Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation114
This paper proposes a robust semi-supervised subspace clustering method based on non-negative LRR (NNLRR) and introduces an efficient linearized alternating direction method with adaptive penalty to solve the corresponding optimization problem.
- Face recognition by sparse discriminant analysis via joint L2,1-norm minimization114
Experiments on three standard face databases illustrate FLDA and SDA via L 2,1 -norm penalty term can significantly improve their recognition performance, and obtain inspiring results with low computation cost and for low-dimension feature.
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- Multigranulation rough-fuzzy clustering based on shadowed sets93
By integrating the notions of shadowed sets and multigranulation into rough-fuzzy clustering approaches, the overall topology of data can be captured well and the uncertain information implicated inData can be effectively addressed, including the uncertainty generated by fuzzification coefficient.
- Discriminative deep multi-task learning for facial expression recognition90
A discriminative DMTL (DDMTL) facial expression recognition method, which overcomes the above shortcomings by considering both the class label information and the samples’ local spatial distribution information simultaneously.
- Projected fuzzy C-means clustering with locality preservation90
An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM, thus enhancing the capability of fuzzy C-means (FCM) for handling high-dimensional data.
- A general tensor representation framework for cross-view gait recognition90
The results demonstrate that the performances of the proposed methods are superior to existing state-of-the-art cross-view gait recognition approaches.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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