8 citations · 33 across the 15 of their papers we have counts for
6 papers · 1 filter
Low-Rank Matrix Recovery from Noise via an MDL Framework-based Atomic Norm
Anyong Qin, Lina Xian, Yongliang Yang +2
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest. However, in many low-level vision problems,…
Learning a Deep Part-based Representation by Preserving Data Distribution
Anyong Qin, Zhaowei Shang, Zhuolin Tan +2
Unsupervised dimensionality reduction is one of the commonly used techniques in the field of high dimensional data recognition problems. The deep autoencoder network which constrai…
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
Lefei Zhang, Qian Zhang, Bo Du +3
In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morp…
Constrained Manifold Learning for Hyperspectral Imagery Visualization
Danping Liao, Yuntao Qian, Yuan Yan Tang
Displaying the large number of bands in a hyper- spectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall conv…
Modal Regression based Atomic Representation for Robust Face Recognition
Yulong Wang, Yuan Yan Tang, Luoqing Li +1
Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the c…
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
Yuewei Lin, Jing Chen, Yu Cao +4
This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all…