Data analysis using discrete cubical homology
By: Chris Kapulkin, Nathan Kershaw
Potential Business Impact:
Finds hidden patterns in weather and money.
We present a new tool for data analysis: persistence discrete homology, which is well-suited to analyze filtrations of graphs. In particular, we provide a novel way of representing high-dimensional data as a filtration of graphs using pairwise correlations. We discuss several applications of these tools, e.g., in weather and financial data, comparing them to the standard methods used in the respective fields.
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