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An algorithm for reducing the dimension and size of a sample for data exploration procedures

Kulczycki P., Łukasik S.

International Journal of Applied Mathematics and Computer Science

2014

Vol. 24, no. 1

133--149

The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).

Reducing the spatial signal correlation for the compression of multilead electrocardiographic recordings

Augustyniak P.

Journal of Medical Informatics & Technologies

2001

Vol. 2, Part. 2

MT107--MT115

This paper is devoted to the transform-based method for decorrelation of simultaneously recorded ECG channels. The conventional 12-lead ECG recordings, due to the non-optimal lead positioning, contain highly redundant data. Eliminating this redundancy yields new possibilities for lossless coding of the ECG, meeting the most severe expectations about the quality of stored signal. The statistical properties featured by uncorrelated signals in the transform domain are more appropriate for the data distribution-based coding techniques. In our work four linear transforms are studied and numerically verified with use of the real ECG data. Additionally, the combination of spatial and temporal decorreation is proposed and discussed as the practical and lossless method for a real implementation. The compression efficiency significantly exceeds the values obtained with use of general-purpose lossless algorithms.