Distributed principal component analysis on networks via directed graphical models

By Zhaoshi Meng, Ami Wiesel, A. Hero. 2012

25 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Kumar Kshitij Patel
2. Boaz Nadler
3. Aymeric Dieuleveut
4. Jonathan D. Rosenblatt
5. Francis R. Bach
6. Shira Kritchman
7. Yunjin Chen
8. Debashis Paul

In Computer Vision and Pattern Recognition

1. Boaz Nadler
2. Aymeric Dieuleveut
3. Jonathan D. Rosenblatt
4. Kumar Kshitij Patel
5. Yunjin Chen

In Signal Processing

1. Boaz Nadler
2. Aymeric Dieuleveut
3. Jonathan D. Rosenblatt
4. Kumar Kshitij Patel

Citations in the corpus

• Communication trade-offs for synchronized distributed SGD with large step size (2019, background): “There has also been work in parallelizing stochastic optimization algorithms for specific problems (like PCA) in the past, for e.g., Mcdonald et al. (2009); McDonald et al. (2010); Meng et al. (2012); Zhuang et al. (2013); Li et al. (2014a); Chin et al. (2015); Oh et al. (2015).”
• On the Optimality of Averaging in Distributed Statistical Learning (2014, methodology): “achine-wise bias via bootstrap. A dierent approach is to trade bias for communication. Recent works that reduce the bias by allowing communication between the m machines include the DDPCA algorithm [Meng et al., 2012], which transfers parts of the inverse Hessian between machines and DANE [Shamir et”