Anzeige
Erschienen in:
2021 | OriginalPaper | Buchkapitel
Dimensionality Reduction Algorithm
verfasst von : Wenzhen Li, Qirui Wu, Zhonghan Peng, Kai Chen, Hui Zhang, Houming Shen
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Since the information revolution, human society has developed extremely rapidly. As mankind continues to develop computing technologies for processing information data, it is also accompanied by the explosive growth of information data, and various high-latitude data are constantly being produced. A serious problem brought about by this is how to deal with these large amounts of high latitude data, and extract the information that can be used from it. High-dimensional data compression extraction algorithm is an effective way to solve this problem. This paper mainly studies the sparse principal component analysis algorithm (SPCA) and the edge-group sparse principal component analysis algorithm (ESPCA) based on the principal component analysis algorithm (PCA), a high-dimensional data compression algorithm. And this paper focuses on the theory of edge group sparse principal component analysis algorithm, and successfully reproduces the program, and obtains results consistent with the original text on the simulated data.