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 ...

Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, ...

Specifically, this project is to develop novel indexing methods for massive geospatial data, scalable algorithms based on high performance computing frameworks (e.g., MapReduce), methods for spatial ...

DS-JedAI (Distributed Spatial JedAI) is a system for Holistic Geospatial Interlinking for big geospatial data. In Holistic Geospatial Interlinking, we aim to discover all the topological relations ...

The Spatial Analysis and Data Science team researches and develops geospatial techniques on biodiversity for conservation and to improve people’s livelihoods. ... Developing novel spatial algorithms ...