no code implementations • 25 Sep 2017 • Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu, Zhang Yi, Shuicheng Yan
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).
no code implementations • CVPR 2016 • Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Trace-norm regularization plays an important role in many areas such as machine learning and computer vision.
no code implementations • CVPR 2015 • Shijie Xiao, Wen Li, Dong Xu, DaCheng Tao
In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix).
no code implementations • 10 Mar 2015 • Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR).
no code implementations • 25 Sep 2013 • Xi Peng, Huajin Tang, Lei Zhang, Zhang Yi, Shijie Xiao
In this paper, we propose a unified framework which makes representation-based subspace clustering algorithms feasible to cluster both out-of-sample and large-scale data.
no code implementations • CVPR 2013 • Zinan Zeng, Shijie Xiao, Kui Jia, Tsung-Han Chan, Shenghua Gao, Dong Xu, Yi Ma
Our framework is motivated by the observation that samples from the same class repetitively appear in the collection of ambiguously labeled training images, while they are just ambiguously labeled in each image.