Robust Tensor Analysis with Non-Greedy L1-Norm Maximization
نویسندگان
چکیده
منابع مشابه
Robust Principal Component Analysis with Non-Greedy l1-Norm Maximization
Principal Component Analysis (PCA) is one of the most important methods to handle highdimensional data. However, the high computational complexitymakes it hard to apply to the large scale data with high dimensionality, and the used 2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on 1-normmaximization, which is efficient and robust to outliers. In...
متن کاملRobust Tensor Clustering with Non-Greedy Maximization
Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in realworld datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the out...
متن کاملAvoiding Optimal Mean Robust PCA/2DPCA with Non-Greedy l1-Norm Maximization
1 -Norm Maximization Minnan Luo, Feiping Nie,2⇤ Xiaojun Chang, Yi Yang, Alexander Hauptmann, Qinghua Zheng 1 Shaanxi Province Key Lab of Satellite-Terrestrial Network , Department of Computer Science, Xi’an Jiaotong University, P. R. China. 2 School of Computer Science and Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, P. R. China. Centre for Quantum Co...
متن کاملNon-Greedy L21-Norm Maximization for Principal Component Analysis
Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle highdimensional data. However, due to the high computational complexity of its eigen decomposition solution, it hard to apply PCA to the large-scale data with high dimensionality. Meanwhile, the squared L2-norm based objective makes it sensitive to data outliers. In recent research, the L1-norm maximi...
متن کاملAvoiding Optimal Mean Robust PCA/2DPCA with Non-greedy ℓ1-Norm Maximization
Robust principal component analysis (PCA) is one of the most important dimension reduction techniques to handle high-dimensional data with outliers. However, the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the Euclidean distance based optimal mean for robust PCA with `1-norm. Some studies consider this issue and integrate the estimation of the opti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Radioengineering
سال: 2016
ISSN: 1210-2512
DOI: 10.13164/re.2016.0200