Gradient algorithms for principal component analysis
نویسندگان
چکیده
منابع مشابه
Gradient Algorithms for Principal Component Analysis
The problem of princip~l component analysis of a symmetric matrix (finding a p-dimensional eigenspace associated with the largest p eigenvalues) can be viewed as a smooth optimization problem on a homogeneous space. A solution in terms of the limiting value of a continuous-time dynamical system is presented, A discretization of the dynamical system is proposed that exploits the geometry of the ...
متن کاملSparse Principal Component Analysis: Algorithms and Applications
Sparse Principal Component Analysis: Algorithms and Applications
متن کاملLow complexity adaptive algorithms for Principal and Minor Component Analysis
Article history: Available online xxxx
متن کاملCommunication-efficient Algorithms for Distributed Stochastic Principal Component Analysis
We study the fundamental problem of Principal Component Analysis in a statistical distributed setting in which each machine out of m stores a sample of n points sampled i.i.d. from a single unknown distribution. We study algorithms for estimating the leading principal component of the population covariance matrix that are both communication-efficient and achieve estimation error of the order of...
متن کاملAlgorithms for projection-pursuit robust principal component analysis
Principal Component Analysis (PCA) is very sensitive in presence of outliers. One of the most appealing robust methods for principal component analysis uses the Projection-Pursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The Projection-Pursuit based method for principal component analysis ...
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: The Journal of the Australian Mathematical Society. Series B. Applied Mathematics
سال: 1996
ISSN: 0334-2700,1839-4078
DOI: 10.1017/s033427000001078x