A subspace type incremental two-dimensional principal component analysis algorithm
Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incre...
Main Authors: | Xiaowei Zhang, Zhongming Teng |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-11-01
|
Series: | Journal of Algorithms & Computational Technology |
Online Access: | https://doi.org/10.1177/1748302620973531 |
Similar Items
-
An Incremental Two-Dimensional Principal Component Analysis for Object Recognition
by: Weimin Ge, et al.
Published: (2018-01-01) -
Incremental algorithms for multilinear principal component analysis of tensor objects
by: Cao, Zisheng, et al.
Published: (2015) - A Block Incremental Algorithm for Computing Dominant Singular Subspaces
-
Generalized Principal Component Analysis-Based Subspace Decomposition of Fault Deviations and Its Application to Fault Reconstruction
by: Boyang Du, et al.
Published: (2020-01-01) -
Incremental Kernel Principal Components Subspace Inference With Nyström Approximation for Bayesian Deep Learning
by: Yongguang Wang, et al.
Published: (2021-01-01)