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- W2907802657 abstract "This chapter overviews joint basis and joint best-basis for data sets. Sets of signals are used for calibration purposes, to solve supervised and unsupervised classification problems, in mixture analysis. Chemometrics deals with sets of signals. All these chemometrical techniques require uniform data presentation. Moreover, the wavelet transform allows transformation of the data from the original domain into the frequency-time, or the scale-time domain. New features—that is, wavelet coefficients, describe local phenomena in a very efficient way and, moreover, allow data compression. The chapter further explains difference between the compression of individual signals and of a data set as a whole. In the case of an individual signal, one is looking for the basis optimal for its compression, whereas while dealing with the data set, a joint basis for the compression of all signals has to be found. Only a joint basis allows the uniform data representation required for further processing by chemometrical techniques. Bases, which are optimal for the individual signals from a given set, can differ to a great extent. To have a uniform representation of the signals in the scale-time domain, a joint basis has to be found, which is optimal in the statistical sense for all the signals. This chapter presents a methodology to select such a basis." @default.
- W2907802657 created "2019-01-11" @default.
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- W2907802657 date "2000-01-01" @default.
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- W2907802657 title "Joint Basis and Joint Best-basis for Data Sets" @default.
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- W2907802657 doi "https://doi.org/10.1016/s0922-3487(00)80032-5" @default.
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