<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The results of the application of chemometric methods, such as principal component analysis (PCA) and its generalization for N-way data, the Tucker3 model, for the analysis of an environmental data set are presented. The analyzed data consists of concentration values of chemical compounds of organic matter, and their transformed products, in a short-term study of a sea water column measured at the Gdańsk Deep (φ = 55°1'N, λ = 19°10'E).</p><p>The main goal of this paper is to present improved approaches for exploration of data sets with missing elements, based on the expectation-maximization (EM) algorithm. The most common methods for dealing with missing data, generally consisting of setting the missing elements to zero or to mean values of the measured data, are often unacceptable as they destroy data correlations or influence interpretation of relationships between objects and variables. The EM algorithm may be built into different computational procedures used for exploratory analysis (i.e. EM/PCA or EM/TUCKER3).</p>
</abstract>

The results of the application of chemometric methods, such as principal component analysis (PCA) and its generalization for N-way data, the Tucker3 model, for the analysis of an environmental data set are presented. The analyzed data consists of concentration values of chemical compounds of organic matter, and their transformed products, in a short-term study of a sea water column measured at the Gdańsk Deep (φ = 55°1'N, λ = 19°10'E).The main goal of this paper is to present improved approaches for exploration of data sets with missing elements, based on the expectation-maximization (EM) algorithm. The most common methods for dealing with missing data, generally consisting of setting the missing elements to zero or to mean values of the measured data, are often unacceptable as they destroy data correlations or influence interpretation of relationships between objects and variables. The EM algorithm may be built into different computational procedures used for exploratory analysis (i.e. EM/PCA or EM/TUCKER3).

Chemometric exploration of sea water chemical component data sets with missing elements

Oceanological and Hydrobiological Studies

The results of the application of chemometric methods, such as principal component analysis (PCA) and its generalization for N-way data, the Tucker3 model, for...

Chemometric exploration of sea water chemical component data sets with missing elements

Online veröffentlicht: 08. Okt. 2008

Seitenbereich: 49 - 62

DOI: https://doi.org/10.2478/v10009-008-0005-1

Schlüsselwörter
missing data, chemometric methods, expectation-maximization algorithm, PCA, Tucker3 model

This content is open access.

Chemometric exploration of sea water chemical component data sets with missing elements

Online veröffentlicht: 08. Okt. 2008

Seitenbereich: 49 - 62

DOI: https://doi.org/10.2478/v10009-008-0005-1

Schlüsselwörtermissing data, chemometric methods, expectation-maximization algorithm, PCA, Tucker3 model

This content is open access.

Schlüsselwörter
missing data, chemometric methods, expectation-maximization algorithm, PCA, Tucker3 model