@inproceedings{wilkerson_chintakunta_krim_2014, title={Computing persistent features in big data: A distributed dimension reduction approach}, DOI={10.1109/icassp.2014.6853548}, abstractNote={Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational complexity of finding the persistent homology of a data set, we develop a simplicial collapse algorithm called the selective collapse. This algorithm works by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. Finally, we demonstrate the savings in computational complexity using geometric random graphs.}, booktitle={International conference on acoustics speech and signal processing}, author={Wilkerson, A. C. and Chintakunta, H. and Krim, H.}, year={2014} }
 @inproceedings{wilkerson_moore_swami_krim_2013, title={Simplifying the homology of networks via strong collapses}, DOI={10.1109/icassp.2013.6638666}, abstractNote={There has recently been increased interest in applications of topology to areas ranging from control and sensing, to social network analysis, to high-dimensional point cloud data analysis. Here we use simplicial complexes to represent the group relationship structure in a network. We detail a novel algorithm for simplifying homology and “hole location” computations on a complex by reducing it to its core using a strong collapse. We show that the homology and hole locations are preserved and provide motivation for interest in this reduction technique with applications in sensor and social networks. Since the complexity of finding “holes” is quintic in the number of simplices, the proposed reduction leads to significant savings in complexity.}, booktitle={International conference on acoustics speech and signal processing}, author={Wilkerson, A. C. and Moore, T. J. and Swami, A. and Krim, H.}, year={2013}, pages={5258–5262} }