Extraction of Components with Structured Variance

Abstract— We present a method for exploratory data analysis of large spatiotemporal data sets such as global longtime climate measurements, extending our previous work on semiblind source separation of climate data. The method seeks fast changing components whose variances exhibit slow behavior with specific temporal structure. The algorithm is developed in the framework of denoising source separation. It finds sources iteratively and alternates between estimating the variance structure of extracted sources and using the structure to find new source estimates. The performance of the algorithm is first demonstrated on a simple example of a semiblind source separation problem with artificially generated signals. Then, the proposed technique is applied to the global surface temperature measurements coming from the NCEP/NCAR reanalysis project. Fast changing temperature components whose variances have prominent annual and decadal structures are extracted. The extracted annual compon...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Alexander Ilin, Harri Valpola, Erkki Oja
• Keywords: Fast Changing Components, IJCNN 2006, Semiblind Source Separation, Source Separation