Paper Info
Title
Online Principal Component Analysis for Evolving Data Streams.
Abstract
We consider an online version of the Principal Component Analysis (PCA), where the goal is to keep track of a subspace of small dimension which captures most of the variance of the data arriving sequentially in a stream. We assume the data stream is evolving and hence the target subspace is changing over time. We cast this problem as a prediction problem, where the goal is to minimize the total compression loss on the data sequence. We review the most popular methods for online PCA and show that the state-of-the-art IPCA algorithm is unable to track the best subspace in this setting. We then propose two modifications of this algorithm, and show that they exhibit a much better predictive performance than the original version of IPCA. Our algorithms are compared against other popular method for online PCA in a computational experiment on real data sets from computer vision.
Year
Venue
Field
2018
ISCIS
Data mining,Data set,Data stream mining,Subspace topology,Data stream,Computer science,Principal component analysis,Distributed computing
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Monika Grabowska100.34
Wojciech Kotlowski215816.32