Multigraph fusion for dynamic graph convolutional network
Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …
structure information of the data to conduct representation learning, but its robustness is …
A review of image set classification
ZQ Zhao, ST Xu, D Liu, WD Tian, ZD Jiang - Neurocomputing, 2019 - Elsevier
In computer vision, we generally solve a classification problem by a single image. With the
video cameras being widely used in our real life, it is a nature choice to solve a classification …
video cameras being widely used in our real life, it is a nature choice to solve a classification …
Global and local structure preservation for feature selection
The recent literature indicates that preserving global pairwise sample similarity is of great
importance for feature selection and that many existing selection criteria essentially work in …
importance for feature selection and that many existing selection criteria essentially work in …
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching
A convenient way of dealing with image sets is to represent them as points on
Grassmannian manifolds. While several recent studies explored the applicability of …
Grassmannian manifolds. While several recent studies explored the applicability of …
Semi-supervised local multi-manifold isomap by linear embedding for feature extraction
In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
[PDF][PDF] Discriminant analysis for dimensionality reduction: An overview of recent developments
Many biometric applications such as face recognition involve data with a large number of
features [1–3]. Analysis of such data is challenging due to the curse-ofdimensionality [4, 5] …
features [1–3]. Analysis of such data is challenging due to the curse-ofdimensionality [4, 5] …
Global and local similarity learning in multi-kernel space for nonnegative matrix factorization
Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …
and local similarity information from data. In this paper, we propose a novel local similarity …
Enhancing low-rank subspace clustering by manifold regularization
Recently, low-rank representation (LRR) method has achieved great success in subspace
clustering, which aims to cluster the data points that lie in a union of low-dimensional …
clustering, which aims to cluster the data points that lie in a union of low-dimensional …
Global and local structure preserving nonnegative subspace clustering
H Jia, D Zhu, L Huang, Q Mao, L Wang, H Song - Pattern Recognition, 2023 - Elsevier
Most subspace clustering methods construct the similarity matrix based on self-expressive
property and apply the spectral relaxation on the similarity matrix to get the final clusters …
property and apply the spectral relaxation on the similarity matrix to get the final clusters …