Abstract
In this paper, the seminal method proposed by Abraham Lempel and Jacob Ziv, aimed at the complexity analysis of sequences of symbols, was modified to compare similarities between two sequences. This modification allowed the creation of a new criterion which can replace likelihood in some pattern recognition applications. Moreover, to allow for analysis and comparison of multivariate continuously valued patterns, we also present a simple adaptation of the Lempel-Ziv's method to time-sampled signals. To illustrate the usefulness of these proposed tools, two sets of experimental results are presented, namely: one on speaker identity verification (biometrics) and another on healthcare signal detection. Both experiments yield promising performances. Moreover, as compared to a conventional pattern recognition method, the new approach provided better performances in terms of Equal Error Ratio in speaker verification experiments.
Highlights
Ziv [1] proposed an approach for complexity analysis of symbol sequences
An important aspect of their approach is the lack of a priori with regard to the source of symbols, which clearly contrasts with the measurement of Shannon entropy [2]
We address multivariate signal comparison with a LZ-like method
Summary
Ziv [1] proposed an approach for complexity analysis of symbol sequences. The original method by Abraham Lempel and Jacob Ziv was aimed at symbolic sequence analysis, but it may be adapted to work with sequences of numbers as well (e.g. sampled signals). This adaptation can be done rather straightforwardly through simple quantization of the signal, mapping it back into a sequence of labels (one label per quantization level).
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