Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation

Principal Subspace Analysis (PSA)-and its sibling, Principal Component Analysis (PCA)-is one of the most popular approaches for dimensionality reduction in signal processing and machine learning. But centralized PSA/PCA solutions are fast becoming irrelevant in the modern era of Big Data, in which t...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on signal and information processing over networks Ročník 7; s. 699 - 715
Hlavní autoři:	Gang, Arpita, Xiang, Bingqing, Bajwa, Waheed U.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Big Data Covariance matrices Dimensionality reduction Distributed data Eigenvalues and eigenfunctions Machine learning Network topologies orthogonal iteration Partitioning algorithms Principal component analysis Principal components analysis principal subspace Signal processing straggler effect Subspaces
ISSN:	2373-776X, 2373-7778
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!