Sparse Models for the Decomposition of Spectral Data
University of Leipzig
Measurement systems in the life sciences are frequently
based on spectral data. Thereby the measured substances
are in general a mixture and hence also the measured
signals are mixed spectra. The deconvolution of such signal
is challenging and different approaches have been proposed
to decompose signal by means of blind source separation.
Taking the measurement technique into account alternative
approaches focusing on non-negative sparse representations
can be expected to be more effective. The talk provides
an introduction into the field of signal decomposition
by different kinds of techniques and gives examples
for the analysis of mass spectrometric data.
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