Graph learningOptimizationSpectral feature selectionPrevious spectral feature selection methods generate the similarity graph via ignoring the negative effect of noise and redundancy of the original feature space, and ignoring the association between g
Currently, multi-label feature selection with joint manifold learning and linear mapping has received much attention. However, the low-quality graph matrix used by existing methods leads to model limitations. Traditional linear mapping cannot learn the coupling relationship between different outputs. In a...
LRDG achieves full constraints on pseudo-label.A pseudo-label based dynamic graph is designed to constrain the feature weights.Latent representation learning is used to mine reliable label information.A convergent algorithm is designed to optimally solve LRDG.关键词: Multi-label learning Feature selecti...
Feature computation and exhaustive search have significantly restricted the speed of graph-based dependency parsing. We propose a faster framework of dynamic feature selec- tion, where features are added sequentially as needed, edges are pruned early, and decisions are made online for each sentence. ...
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Examining existing methods for emotion recognition reveals that the high spatial resolution of the EEG signal is crucial for extracting sufficient information in the feature selection and extraction process for emotion recognition. As mentioned above, the mapping of the EEG signal from the scalp sensor...
1. It employs a two-stage transfer learning strategy for learning, with a dynamic graph convolution model for training implementation. For the two-stage transfer learning, they are manifested in the feature extraction module (Fig. 1a) and model training module (Fig. 1b). In the feature ...
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D. (2023). A region-based randers geodesic approach for image segmentation. International Journal of Computer Vision, 1–43. Chen, Y., Wu, L., & Zaki, M. (2020). Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Advances in Neural Information ...
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