Malicious Corruption-Resilient Wide-Area Oscillation Monitoring Using Principal Component Pursuit
نویسندگان
چکیده
منابع مشابه
DUAL PRINCIPAL COMPONENT PURSUIT Dual Principal Component Pursuit
We consider the problem of outlier rejection in single subspace learning. Classical approaches work with a direct representation of the subspace, and are thus efficient when the subspace dimension is small. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such it is particularly suitable for high-dimensional subspaces. We pose th...
متن کاملusing etm+ band ratios and principal component analysis for monitoring of vegetation cover in neyshabour area
remote sensing techniques are known as very useful methods for studying land use management in arid and semi arid areas, where undeveloped soils are dominant. these techniques can be used for determining different environmental characteristics including soil and vegetations. in this study etm+ band ratios and principal component analysis (pca) were used for monitoring of vegetation cover and it...
متن کاملRobust Principal Component Analysis by Projection Pursuit
Different algorithms for principal component analysis (PCA) based on the idea of projection pursuit are proposed. We show how the algorithms are constructed, and compare the new algorithms with standard algorithms. With the R implementation pcaPP we demonstrate the usefulness at real data examples. Finally, it will be outlined how the algorithms can be used for robustifying other multivariate m...
متن کاملStrongly Convex Programming for Principal Component Pursuit
In this paper, we address strongly convex programming for principal component pursuit with reduced linear measurements, which decomposes a superposition of a low-rank matrix and a sparse matrix from a small set of linear measurements. We first provide sufficient conditions under which the strongly convex models lead to the exact low-rank and sparse matrix recovery; Second, we also give suggesti...
متن کاملStable Analysis of Compressive Principal Component Pursuit
Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove that the solution to the related convex programming of CPCP gives an estimate that is stable to small en...
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: IEEE Transactions on Smart Grid
سال: 2019
ISSN: 1949-3053,1949-3061
DOI: 10.1109/tsg.2017.2778054