@article{krim_gentimis_chintakunta_2016, title={DISCOVERING THE WHOLE BY THE COARSE A topological paradigm for data analysis}, volume={33}, ISSN={["1558-0792"]}, DOI={10.1109/msp.2015.2510703}, abstractNote={The increasing interest in big data applications is ushering in a large effort in seeking new, efficient, and adapted data models to reduce complexity, while preserving maximal intrinsic information. Graph-based models have recently been getting a lot of attention on account of their intuitive and direct connection to the data [43]. The cost of these models, however, is to some extent giving up geometric insight as well as algebraic flexibility.}, number={2}, journal={IEEE SIGNAL PROCESSING MAGAZINE}, author={Krim, Hamid and Gentimis, Thanos and Chintakunta, Harish}, year={2016}, month={Mar}, pages={95–104} }
@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} }
@article{chintakunta_krim_2014, title={Distributed Localization of Coverage Holes Using Topological Persistence}, volume={62}, ISSN={["1941-0476"]}, DOI={10.1109/tsp.2014.2314063}, abstractNote={We develop distributed algorithms to detect and localize coverage holes in sensor networks. We neither assume coordinate information of the nodes, neither any distances between the nodes. We use algebraic topological methods to define a coverage hole, and develop provably correct algorithm to detect a hole. We then partition the network into smaller subnetworks, while ensuring that the holes are preserved, and checking for holes in each. We show that repeating this process leads to localizing the coverage holes. We demonstrate the improved complexity of our algorithm using simulations.}, number={10}, journal={IEEE TRANSACTIONS ON SIGNAL PROCESSING}, author={Chintakunta, Harish and Krim, Hamid}, year={2014}, month={May}, pages={2531–2541} }
@inproceedings{emrani_chintakunta_krim_2014, title={Real time detection of harmonic structure: A case for topological signal analysis}, DOI={10.1109/icassp.2014.6854240}, abstractNote={The goal of this study is to find evidence of cyclicity or periodicity in data with low computational complexity and high accuracy. Using delay embeddings, we transform the timedomain signal into a point cloud, whose topology reflects the periodic behavior of the signal. Persistent homology is employed to determine the underlying manifold of the point cloud, and the Euler characteristic provides for a fast computation of topology of the resulting manifold. We apply the introduced approach to breathing sound signals for wheeze detection. Our experiments substantiate the capabilities of the proposed method.}, booktitle={International conference on acoustics speech and signal processing}, author={Emrani, S. and Chintakunta, H. and Krim, H.}, year={2014} }