Search Results for author:
Found 4 papers, 1 papers with code
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis
While PCA is often thought of as a dimensionality reduction method, the purpose of PCA is actually two-fold: dimension reduction and uncorrelated feature learning.
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation
This has led to the study of distributed PSA/PCA solutions, in which the data are partitioned across multiple machines and an estimate of the principal subspace is obtained through collaboration among the machines.
A Linearly Convergent Algorithm for Distributed Principal Component Analysis
This paper focuses on the dual objective of PCA, namely, dimensionality reduction and decorrelation of features, but in a distributed setting.
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework.
