TY - GEN
T1 - A robust incremental principal component analysis for feature extraction from stream data with missing values
AU - Aoki, Daijiro
AU - Omori, Toshiaki
AU - Ozawa, Seiichi
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In this paper, we propose a robust incremental principal component analysis (IPCA) for stream data that can handle missing values on an ongoing basis. In the proposed IPCA, a missing value is substituted with the value estimated from a conditional probability density function. The conditional probability density functions are incrementally updated when new data are given. In the experiments, we evaluate the performance for both artificial and real data sets through the comparison with the two conventional approaches to handing missing values. We first investigate the estimation errors of missing values. The experimental results demonstrate that the proposed IPCA gives lower estimation errors compared to the other approaches. Next, we investigate the approximation accuracy of eigenvectors. The results show that the proposed IPCA has relatively good accuracy of eigenvectors not only for major components but also for minor components.
AB - In this paper, we propose a robust incremental principal component analysis (IPCA) for stream data that can handle missing values on an ongoing basis. In the proposed IPCA, a missing value is substituted with the value estimated from a conditional probability density function. The conditional probability density functions are incrementally updated when new data are given. In the experiments, we evaluate the performance for both artificial and real data sets through the comparison with the two conventional approaches to handing missing values. We first investigate the estimation errors of missing values. The experimental results demonstrate that the proposed IPCA gives lower estimation errors compared to the other approaches. Next, we investigate the approximation accuracy of eigenvectors. The results show that the proposed IPCA has relatively good accuracy of eigenvectors not only for major components but also for minor components.
UR - http://www.scopus.com/inward/record.url?scp=84893545038&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893545038&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6706771
DO - 10.1109/IJCNN.2013.6706771
M3 - Conference contribution
AN - SCOPUS:84893545038
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -