With the advent of the big data era, data becomes increasingly important, especially for many machine learning tasks where data quality is vital. However, missing data is inevitable in many real-world applications, making its handling a challenging problem. Although some reviews exist, they either treat it as a statist...
Jing-Xuan Li, Wei-Ping Ding, Xing-Quan Zhu et al.· Machine Learning and Knowled...· 0 citations
INTRODUCTION
Gene feature selection is essential in bioinformatics and medical research, as it identifies gene subsets closely associated with specific diseases or biological traits from high-dimensional gene datasets. High-dimensional gene data causes the curse of dimensionality, leading to sparsity, complex inter-fea...
Zhilin Wangy, Wei-Ping Ding, Jin-Quan Zhang et al.· Journal of Advanced Research· 0 citations
Results indicate that PIQGNN provides an efficient, scalable, and noise-resilient quantum framework for graph learning, highlighting its practical potential in the noisy intermediate-scale quantum (NISQ) era.
Maoduo Li, Wen Liu, Lei Shi et al.· IEEE Transactions on Neural...· 0 citations
Hashing algorithms represent data by generating compact binary hash codes, enabling efficient cross-modal similarity search and significantly improving the storage efficiency and retrieval performance of image-text data. However, because traditional hashing methods typically separate image-text feature extraction from...
Shuang Zhang, Yue Wu, Lei Shi et al.· IEEE Transactions on Knowled...· 0 citations
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