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- Yi-Dong ShenSuggested from co-authorship
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Works30 from public data
- Recovery of corrupted multiple kernels for clustering50
This paper proposes a novel method for learning a robust yet low-rank kernel for clustering tasks, observing that the noises of each kernel have specific structures, so it can make full use of them to clean multiple input kernels and then aggregate them into a robust, low- rank consensus kernel.
- Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization47
A novel robust clustering ensemble method which develops a block coordinate descent algorithm which is theoretically guaranteed to converge and captures the sparse and symmetric errors and integrates them into the robust and consensus framework to learn a low-rank matrix.
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- Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision21
A novel uncertainty sampling algorithm for action recognition using expected Average Precision, defined as the area under the precision-recall curve is proposed and shown to outperforms other uncertainty-based active learning algorithms.
- Bounding Uncertainty for Active Batch Selection16
This work bound the certainty scores of unlabeled samples from below and directly combine this lower-bounded certainty with representativeness in the objective function, and shows that the two aforementioned approaches are mathematically equivalent to two special cases of the approach.
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- Convex Batch Mode Active Sampling via α-Relative Pearson Divergence11
This paper proposes a novel approach to selecting the optimal batch of queries by minimizing the α-relative Pearson divergence between the labeled and the original datasets and finds that the objective has an equivalent convex form, and thus a global optimal solution can be obtained.
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- Multi-Omics Joint Analysis of Molecular Mechanisms of Compound Essential Oils Inhibiting Spoilage Yeast in Paocai4
The results of this study provided new insights into the mechanism of P. manshurica inhibition by CEOs, and provide a reference basis for the development of food-related bacteriostatic agents by CEOs.
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- Overview of Sign Language Translation Based on Natural Language Processing1
This paper explores the progress, challenges, and future directions in Sign Language Translation (SLT) within the broader field of Sign Language Processing (SLP), which combines Computer Vision and Natural Language Processing (NLP) to translate sign language videos into spoken language texts.
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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 2026-10-11. 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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