Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
نویسندگان
چکیده
منابع مشابه
Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to routinely perform tasks like principal component analysis (PCA). Recursive algorithms that update the PCA with each new observation have been studied in various fields of research and found wide applications in industrial monitoring, computer vision, astronomy, and lat...
متن کاملOnline Principal Component Analysis
We consider the online version of the well known Principal Component Analysis (PCA) problem. In standard PCA, the input to the problem is a set of vectors X = [x1, . . . , xn] in Rd×n and a target dimension k < d; the output is a set of vectors Y = [y1, . . . , yn] in Rk×n that minimize minΦ ‖X − ΦY ‖F where Φ is restricted to be an isometry. The global minimum of this quantity, OPTk, is obtain...
متن کاملDimension Reduction by Local Principal Component Analysis
Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear ap...
متن کاملFeature Dimension Reduction of Multisensor Data Fusion using Principal Component Fuzzy Analysis
These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. O...
متن کاملHigh-dimensional Principal Component Analysis
High-dimensional Principal Component Analysis by Arash Ali Amini Doctor of Philosophy in Electrical Engineering University of California, Berkeley Associate Professor Martin Wainwright, Chair Advances in data acquisition and emergence of new sources of data, in recent years, have led to generation of massive datasets in many fields of science and engineering. These datasets are usually characte...
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: International Statistical Review
سال: 2017
ISSN: 0306-7734
DOI: 10.1111/insr.12220