132 citations · 1.3k across the 52 of their papers we have counts for
12 papers · 1 filter
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…
A hybrid model-based and learning-based approach for classification using limited number of training samples
Alireza Nooraiepour, Waheed U. Bajwa, Narayan B. Mandayam
The fundamental task of classification given a limited number of training data samples is considered for physical systems with known parametric statistical models. The standalone l…
A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression
Batoul Taki, Mohsen Ghassemi, Anand D. Sarwate +1
This paper considers the problem of matrix-variate logistic regression. It derives the fundamental error threshold on estimating low-rank coefficient matrices in the logistic regre…
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…
Scaling-up Distributed Processing of Data Streams for Machine Learning
Matthew Nokleby, Haroon Raja, Waheed U. Bajwa
Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the le…