Data interpretation labels

Numeric-to-symbohc transformations are used in pattern-recognition problems where the network is used to classify input data vectors into specific labeled classes. Pattern recognition problems include data interpretation, feature identification, and diagnosis. 

In this publication, the purpose of data analysis is to drive toward data interpretation that consists of assigning various types of labels. These label... 

Fig. 5. Label class decision methods for data interpretation.

Data Interpretation extends data analysis techniques to label assignment and considers both integrated approaches to feature extraction and feature mapping and approaches with explicit and separable extraction and mapping steps. The approaches in this section focus on those that form numeric-symbolic interpreters to map from numeric data to specific labels of interest. 

This chapter provides a complementary perspective to that provided by Kramer and Mah (1994). Whereas they emphasize the statistical aspects of the three primary process monitoring tasks, data rectification, fault detection, and fault diagnosis, we focus on the theory, development, and performance of approaches that combine data analysis and data interpretation into an automated mechanism via feature extraction and label assignment. 

The objective functions for both k-means clustering and the F-nearest neighbor heuristic given by Eqs. (20) and (21) use information only from the inputs. Because of this capacity to cluster data, local methods are particularly useful for data interpretation when the clusters can be assigned labels. 

By definition, the exemplar patterns used by these algorithms must be representative of the various pattern classes. Performance is tied directly to the choice and distribution of these exemplar patterns. In light of the high dimensionality of the process data interpretation problem, these approaches leave in question how reasonable it is to accurately partition a space such as R6+ (six-dimensional representation space) using a finite set of pattern exemplars. This degradation of interpretation performance as the number of possible labels (classes) increases is an issue of output dimensionality. 

Nonlinear methods based on linear projection also can be used for data interpretation. Since these methods require numeric inputs and outputs, the symbolic class label can be converted into a numeric value for their training. Proposed applications involving numeric to symbolic transformations have a reasonably long history (e.g., Hoskins and Himmel-... 

The knowledge required to implement Bayes formula is daunting in that a priori as well as class conditional probabilities must be known. Some reduction in requirements can be accomplished by using joint probability distributions in place of the a priori and class conditional probabilities. Even with this simplification, few interpretation problems are so well posed that the information needed is available. It is possible to employ the Bayesian approach by estimating the unknown probabilities and probability density functions from exemplar patterns that are believed to be representative of the problem under investigation. This approach, however, implies supervised learning where the correct class label for each exemplar is known. The ability to perform data interpretation is determined by the quality of the estimates of the underlying probability distributions. 

If there is sufficient operating history to provide the labeled data and sufficient resources to label it, then it is desirable to rely on this information for data interpretation because it reflects the actual operation. With respect... 

With isotopically-labelled compounds, one has to pay attention to heavy-isotope effects, that may result in different retention times for light- and heavy-ICAT labelled peptides, especially in the case of D-labelling [14, 85], The position of the D-label in the molecule is important as well the least isotopic retention-time shift is observed, when the H-labels are positioned at a polar group with little interaction with the hydrophobic RPLC packing. The importance of the isotope effect in data interpretation should not be underestimated. Labelling with causes no observable retention-time shifts. More recently, ICAT labels have been introduced containing instead of in order to reduce the isotope effects (clCAT) [86-87]. 

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