FuncICA for Time Series Pattern Discovery.

We introduce FuncICA, a new independent component analysis method for pattern discovery in inherently functional data, such as time series data. FuncICA can be considered an analog to functional principal component analysis, where instead of extracting components to minimize L2 reconstruction error, we maximize independence of the components over the functional observations. We develop an algorithm for extracting independent component curves and offer a method for optimizing a smoothing parameter. Results for synthetic, gene expression, and event-related potential data indicate that FuncICA can recover well-known phenomena and improve classification accuracy, highlighting the utility of FuncICA for unsupervised learning in temporal data.

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Alexander Gray, Nishant Mehta
• Keywords: Component Analysis Method, Computer Science, Functional Principal Component, Independent Component Curves, SDM 2009