Novel Regression Methods For Spectral Data
Kayondo, Wasswa Hassan (2012)
Diplomityö
Kayondo, Wasswa Hassan
2012
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe201206055761
https://urn.fi/URN:NBN:fi-fe201206055761
Tiivistelmä
Singular Value Decomposition (SVD), Principal Component Analysis (PCA)
and Multiple Linear Regression (MLR) are some of the mathematical pre-
liminaries that are discussed prior to explaining PLS and PCR models. Both
PLS and PCR are applied to real spectral data and their di erences and
similarities are discussed in this thesis. The challenge lies in establishing the
optimum number of components to be included in either of the models but
this has been overcome by using various diagnostic tools suggested in this
thesis. Correspondence analysis (CA) and PLS were applied to ecological
data. The idea of CA was to correlate the macrophytes species and lakes.
The di erences between PLS model for ecological data and PLS for spectral
data are noted and explained in this thesis.
i
and Multiple Linear Regression (MLR) are some of the mathematical pre-
liminaries that are discussed prior to explaining PLS and PCR models. Both
PLS and PCR are applied to real spectral data and their di erences and
similarities are discussed in this thesis. The challenge lies in establishing the
optimum number of components to be included in either of the models but
this has been overcome by using various diagnostic tools suggested in this
thesis. Correspondence analysis (CA) and PLS were applied to ecological
data. The idea of CA was to correlate the macrophytes species and lakes.
The di erences between PLS model for ecological data and PLS for spectral
data are noted and explained in this thesis.
i