A subspace type incremental two-dimensional principal component analysis algorithm

Abstract

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 incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA.

Spatio-Temporal Coding-Based Helicopter Trajectory Planning for Pulsed Neural Membrane System

“…Assume that the set of sample vectors {z(i)} is obtained, the dimensionality is randomized as desired, and the covariance matrix A � E z T (i)z(π) 􏼈 􏼉 is defined, which must satisfy λx � Ax, where λ is the eigenvalues. Replacing x with the estimate x(i) and assuming λx � a, it is obtained that [29,30]…”