Browsing by Author "Harer, J"

Now showing 1 - 4 of 4
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    Fréchet Means for Distributions of Persistence Diagrams
    (Discrete & Computational Geometry, 2014) Turner, K; Mileyko, Y; Mukherjee, S; Harer, J
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    Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
    (Proceedings of the 39th IEEE Aerospace Conference, 2018-03) Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J
    In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explored, including a cutting-edge time-warping technique that can simultaneously handle local time warping and partial matches, while also controlling for the change in geometry between feature spaces of the two modalities. We note that this technique is quite general, and does not depend on the choice of modalities. In this particular setting, we demonstrate that the cross-modal distance between SSMs corresponding to the same trajectory type is smaller than the cross-modal distance between SSMs corresponding to distinct trajectory types, and we formalize this observation via precision-recall metrics in experiments. Finally, we comment on promising implications of these ideas for future integration into multiple-hypothesis tracking systems.
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    Geometric Models for Musical Audio Data
    (Proceedings of the 32st International Symposium on Computational Geometry (SOCG), 2016-06) Bendich, P; Gasparovic, E; Harer, J; Tralie, C
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    Multi-scale local shape analysis and feature selection in machine learning applications
    (Proceedings of the International Joint Conference on Neural Networks, 2015-09-28) Bendich, P; Gasparovic, E; Harer, J; Izmailov, R; Ness, L
    © 2015 IEEE.We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.