Abstract
Principal Components Analysis (PCA) is one of the most frequently used dimensionality reduction methods. PCA is suitable in time-critical case (i.e., when distance calculations involving only a few dimensions can be afforded) [1] . When it comes to image compression, PCA has its significant advantages: good performance in removing of correlations, and high compression ratio. JohnsonLindenstrauss Lemma is a probability method leading to a deterministic statement of dimensionality reduction. This paper proposes a image compression algorithm: PCA for image compression based on improved JohnsonLindenstrauss Lemma.
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 callDisclaimer: 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.
