83 citations · 143 across the 5 of their papers we have counts for
5 papers
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis
Arpita Gang, Waheed U. Bajwa
Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA is often thought of as a dimensionality reduction method, th…
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation
Arpita Gang, Bingqing Xiang, Waheed U. Bajwa
Principal Subspace Analysis (PSA) -- and its sibling, Principal Component Analysis (PCA) -- is one of the most popular approaches for dimensionality reduction in signal processing…
A Linearly Convergent Algorithm for Distributed Principal Component Analysis
Arpita Gang, Waheed U. Bajwa
Principal Component Analysis (PCA) is the workhorse tool for dimensionality reduction in this era of big data. While often overlooked, the purpose of PCA is not only to reduce data…
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
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…
Towards Automated Single Channel Source Separation using Neural Networks
Arpita Gang, Pravesh Biyani, Akshay Soni
Many applications of single channel source separation (SCSS) including automatic speech recognition (ASR), hearing aids etc. require an estimation of only one source from a mixture…