A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis.
Erica RamirezMarkus WimmerMartin AtzmuellerPublished in: KR4HC/ProHealth/TEAAM@AIME (2019)
Keyphrases
- anomaly detection
- computational framework
- multivariate time series
- data analysis
- computational model
- unsupervised learning
- human gait
- machine learning
- dimension reduction
- text classification
- knowledge discovery
- pattern recognition
- feature extraction
- feature selection
- neural network
- data sets
- support vector machine
- cluster analysis
- computer vision
- pattern classification
- data mining
- object recognition
- preprocessing
- support vector
- high dimensional data
- learning algorithm
- gait recognition