Abstract: Low-rank based methods have been widely adopted to structure preserving, when the projection matrix is learned for feature extraction.

ABSTRACT. Low-rank based methods have been widely adopted to struc- ture preserving, when the projection matrix is learned for fea- ture extraction.

We propose a method for learning continuous-space vector representation of graphs, which preserves directed edge information. Previous work in learning ...

Zhuojie Huang, Shuping Zhao, Lunke Fei, Jigang Wu: Weighted Graph Embedded Low-Rank Projection Learning for Feature Extraction. ICASSP 2022: 1501-1505.

Weighted Graph Embedded Low-Rank Projection Learning for Feature Extraction. Zhuojie Huang 1. ,. Shuping Zhao 1. ,. Lunke Fei 1. ,. Jigang Wu 1.

We propose a novel unsupervised feature extraction method called low-rank adaptive graph embedding (LRAGE), which can perform subspace learning and adaptive ...

Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal the underlying correlation structure of data.

ABSTRACT. We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector.

We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or ...

Abstract— Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extrac-.