Orthogonal Feature Learning for Time Series Clustering

This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or features extracted from the data are used as input for various clustering or classification algorithms. Our methods use features extraction to represent a time series by a fixed-dimensional vector whose components are statistical metrics. Each metric is a specific feature based on the global structure of the time series data given. However, if there are correlations between feature metrics, it could result in clustering in a distorted space. To address this, we propose to orthogonalize the space of metrics using linear correlation information to reduce the impact on the clustering from the correlations between clustering inputs. Our method is algorithm and data independent, and we demonstrate the orthogonal feature learning on two popular clustering algorithms, k-means and hierarchical clustering. Two ben...

• Type: Journal
• Year: 2011
• Where: ISNN
• Authors: Xiaozhe Wang, Leo Lopes
• Keywords: Dimensional Vector, ISNN 2011, Linear Correlation, Neural Networks, Time Series Data