Abstract
In order to quickly discover the low-dimensional representation of high-dimensional noisy data in online environments, we transform the linear dimensionality reduction problem into the problem of learning the bases of linear feature subspaces. Based on that, we propose a fast and robust dimensionality reduction framework for incremental subspace learning named evolutionary orthogonal component analysis (EOCA). By setting adaptive thresholds to automatically determine the target dimensionality, the proposed method extracts the orthogonal subspace bases of data incrementally to realize dimensionality reduction and avoids complex computations. Besides, EOCA can merge two learned subspaces that are represented by their orthonormal bases to a new one to eliminate the outlier effects, and the new subspace is proved to be unique. Extensive experiments and analysis demonstrate that EOCA is fast and achieves competitive results, especially for noisy data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE transactions on neural networks and learning systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
