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Found 4 papers, 1 papers with code
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks
Inspired by the success of functional brain networks, we propose a novel framework for interpretability of deep neural networks, that is, the functional network.
Manifold Feature Index: A novel index based on high-dimensional data simplification
In this paper, we propose a novel stock index model, namely the manifold feature(MF) index, to reflect the overall price activity of the entire stock market.
High-Dimensional Data Set Simplification by Laplace-Beltrami Operator
Moreover, we develop feature point detection methods for the functions defined on an unorganized data point set in high-dimensional space, and devise metrics for measuring the fidelity of the simplified data set to the original set.
Persistence B-Spline Grids: Stable Vector Representation of Persistence Diagrams Based on Data Fitting
Many attempts have been made in recent decades to integrate machine learning (ML) and topological data analysis.
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