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Found 5 papers, 1 papers with code
Multi-scale Geometric Summaries for Similarity-based Sensor Fusion
A fused similarity template is then derived from the modality-specific SSMs using a technique called similarity network fusion (SNF).
Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
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.
Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction
We introduce a new algorithm, called CDER, for supervised machine learning that merges the multi-scale geometric properties of Cover Trees with the information-theoretic properties of entropy.
Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications
We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset.
Topological and Statistical Behavior Classifiers for Tracking Applications
We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning.
