C Kanan, G Cottrell - 2010 IEEE Computer Society Conference …, 2010 - ieeexplore.ieee.org
Classification of images in many category datasbets has rapidly improved in recent years. However, systems that perform well on particular datasets typically have one or more …
G Wang, N Shi, Y Shu, D Liu - Neural processing letters, 2016 - Springer
Manifold learning algorithms mainly focus on discovering the intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space. Among them, locally linear …
L Yang, W Gong, X Gu, W Li, Y Liang - Neurocomputing, 2008 - Elsevier
In this paper, we propose a null space discriminant locality preserving projections (NDLPP) method for facial feature extraction and recognition. Based on locality preserving projections …
This paper investigates advanced channel compensation techniques for the purpose of improving i-vector speaker verification performance in the presence of high intersession …
WH Yang, DQ Dai, H Yan - IEEE transactions on knowledge …, 2008 - ieeexplore.ieee.org
High-dimensional data and the small sample size problem occur in many modern pattern classification applications such as face recognition and gene expression data analysis. To …
X Zhai, M Chen - Nanoscale Advances, 2023 - pubs.rsc.org
It is a great challenge to acquire novel Bi2WO6/MIL-53 (Al)(BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space …
X Xiao, Y Chen, YJ Gong, Y Zhou - IEEE Transactions on Image …, 2019 - ieeexplore.ieee.org
Linear discriminant analysis has been incorporated with various representations and measurements for dimension reduction and feature extraction. In this paper, we propose two …