Robust transfer metric learning for image classification
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 …
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 …
dimensional space into the target low-dimension space for feature extraction. However, due …
Linearity-aware subspace clustering
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 …
state-of-the-art methods learn the similarity matrix through self-expressive strategy …
Robust spectral ensemble clustering via rank minimization
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 …
integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition …
From ensemble clustering to subspace clustering: Cluster structure encoding
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 …
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 …
insufficient and those samples involve pose, illumination and expression variations, etc., this …
Robust multiview data analysis through collective low-rank subspace
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 …
and multiple sensors desire to represent the data in a better way. Conventional multiview …
Robust low-rank discovery of data-driven partial differential equations
Partial differential equations (PDEs) are essential foundations to model dynamic processes
in natural sciences. Discovering the underlying PDEs of complex data collected from real …
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 …
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 …
common method. Recently, presentation-based approaches have been widely concerned …