Robust transfer metric learning for image classification

Z Ding, Y Fu - IEEE Transactions on Image Processing, 2016 - ieeexplore.ieee.org
Metric learning has attracted increasing attention due to its critical role in image analysis and
classification. Conventional metric learning always assumes that the training and test data …

Learning latent low-rank and sparse embedding for robust image feature extraction

Z Ren, Q Sun, B Wu, X Zhang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
To defy the curse of dimensionality, the inputs are always projected from the original high-
dimensional space into the target low-dimension space for feature extraction. However, due …

Linearity-aware subspace clustering

Y Xu, S Chen, J Li, J Qian - Proceedings of the AAAI conference on …, 2022 - ojs.aaai.org
Obtaining a good similarity matrix is extremely important in subspace clustering. Current
state-of-the-art methods learn the similarity matrix through self-expressive strategy …

Robust spectral ensemble clustering via rank minimization

Z Tao, H Liu, S Li, Z Ding, Y Fu - ACM Transactions on Knowledge …, 2019 - dl.acm.org
Ensemble Clustering (EC) is an important topic for data cluster analysis. It targets to
integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition …

From ensemble clustering to subspace clustering: Cluster structure encoding

Z Tao, J Li, H Fu, Y Kong, Y Fu - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
In this study, we propose a novel algorithm to encode the cluster structure by incorporating
ensemble clustering (EC) into subspace clustering (SC). First, the low-rank representation …

Face recognition approach by subspace extended sparse representation and discriminative feature learning

M Liao, X Gu - Neurocomputing, 2020 - Elsevier
To address the problem of face recognition where the number of the labeled samples is
insufficient and those samples involve pose, illumination and expression variations, etc., this …

Robust multiview data analysis through collective low-rank subspace

Z Ding, Y Fu - IEEE transactions on neural networks and …, 2017 - ieeexplore.ieee.org
Multiview data are of great abundance in real-world applications, since various viewpoints
and multiple sensors desire to represent the data in a better way. Conventional multiview …

Robust low-rank discovery of data-driven partial differential equations

J Li, G Sun, G Zhao, HL Li-wei - … of the AAAI Conference on Artificial …, 2020 - ojs.aaai.org
Partial differential equations (PDEs) are essential foundations to model dynamic processes
in natural sciences. Discovering the underlying PDEs of complex data collected from real …

Face recognition based on dictionary learning and subspace learning

M Liao, X Gu - Digital Signal Processing, 2019 - Elsevier
Dictionary learning plays an important role in sparse representation based face recognition.
Many dictionary learning algorithms have been successfully applied to face recognition …

Orthogonal low-rank projection learning for robust image feature extraction

X Zhang, Z Tan, H Sun, Z Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Projecting the original data into a low-dimensional target space for feature extraction is a
common method. Recently, presentation-based approaches have been widely concerned …