2 citations · 3 across the 4 of their papers we have counts for
4 papers
Exact multi-parameter persistent homology of time-series data: Fast and variable topological inferences
Keunsu Kim, Jae-Hun Jung
We propose a novel exact multi-parameter persistent homology method for analyzing time-series data utilizing the Liouville torus. In the field of topological data analysis (TDA), t…
Machine Composition of Korean Music via Topological Data Analysis and Artificial Neural Network
Mai Lan Tran, Dongjin Lee, Jae-Hun Jung
Common AI music composition algorithms based on artificial neural networks are to train a machine by feeding a large number of music pieces and create artificial neural networks th…
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection
Sijin Yeom, Jae-Hun Jung
Random cut forest (RCF) algorithms have been developed for anomaly detection, particularly in time series data. The RCF algorithm is an improved version of the isolation forest (IF…
Instability of the Betti Sequence for Persistent Homology and a Stabilized Version of the Betti Sequence
Megan Johnson, Jae-Hun Jung
Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homo…