Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis
For a quantitative analysis of differential protein expression, one has to overcome the problem of aligning time series of measurements from liquid chromatography coupled to mass spectrometry. When repeating experiments one typically observes that the time axis is deformed in a non-linear way. In this paper we propose a technique to align the time series based on generalized canonical correlation analysis (GCCA) for multiple datasets. The monotonicity constraint in time series alignment is incorporated in the GCCA algorithm. The alignment function is learned both in a supervised and a semi-supervised fashion. We compare our approach with previously published methods for aligning mass spectrometry data on a large proteomics dataset.
KeywordsCanonical Correlation Analysis Time Series Alignment Proteomics
Unable to display preview. Download preview PDF.
- 2.Listgarten, J., Neal, R.M., Roweis, S.T., Emili, A.: Multiple alignment of continuous time series. In: NIPS 17, pp. 817–824 (2005)Google Scholar
- 6.Lawson, C.L., Hanson, R.J.: Solving Least Square Problems. Prentice-Hall, Englewood Cliffs (1974)Google Scholar
- 9.Via, J., Santamaria, I., Perez, J.: Canonical correlation analysis (cca) algorithms for multiple data sets: Application to blind simo equalization. In: European Signal Processing Conference (2005)Google Scholar