J
Jianfeng Lu
Researcher at Nanjing University of Science and Technology
Publications - 59
Citations - 1080
Jianfeng Lu is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Gait (human). The author has an hindex of 15, co-authored 46 publications receiving 735 citations.
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Multilabel Image Classification With Regional Latent Semantic Dependencies
TL;DR: Zhang et al. as discussed by the authors proposed a regional latent semantic dependencies model (RLSD) to localize the regions that may contain multiple highly dependent labels, and the localized regions are further sent to the recurrent neural networks to characterize the label dependencies at the regional level.
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Semi-supervised linear discriminant analysis for dimension reduction and classification
TL;DR: This paper proposes a novel method named semi-supervised linear discriminant analysis (SLDA), which can use limited number of labeled data and a quantity of the unlabeled ones for training so that LDA can accommodate to the situation of a few labeled data available.
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Multiple kernel clustering based on centered kernel alignment
TL;DR: This paper proposes a novel MKC method that is different from those popular approaches, and an efficient two-step iterative algorithm is developed to solve the formulated optimization problem.
Posted Content
Multi-Label Image Classification with Regional Latent Semantic Dependencies
TL;DR: The proposed RLSD achieves the best performance compared to the state-of-the-art models, especially for predicting small objects occurring in the images, and can approach the upper bound without using the bounding-box annotations, which is more realistic in the real world.
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An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments
TL;DR: A reformative kernel algorithm, which can deal with two-class problems as well as those with more than two classes, on Fisher discriminant analysis is proposed and a recursive algorithm for selecting "significant nodes", is developed in detail.