An approach to dimensionality reduction in time series

Krawczak, Maciej; Szkatuła, Grażyna

doi:10.1016/j.ins.2013.10.037

Cited by 50 publications

(13 citation statements)

References 33 publications

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“…Furthermore, these dimensionally reduction methods may delete parts of features directly, leading to the loss of sensitive information. As analysed, the basis for feature dimensionality reduction is to evaluate the correlations between various vectors [28]. Correlation coefficient is one traditional factor [29].…”

Section: The Application Of Lcs and Information Entropy As A Novel Fumentioning

confidence: 99%

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

2018

SV-JME

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Section: The Application Of Lcs and Information Entropy As A Novel Fumentioning

confidence: 99%

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

2018

SV-JME

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“…[2], [3], [4], [5]), in this paper we propose the new measure of perturbation of one set by another and therefore this kind of sets' perturbation will be called as perturbation type 2: Definition 1. The measure of perturbation type 2 of set A j by set A i is defined in the following manner:…”

Section: Matching Of Setsmentioning

confidence: 99%

On Perturbation Measure of Sets – Properties

Krawczak¹,

Szkatuła²

2014

JAMRIS

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“…. , 150, [17][18][19][20], • For clustering problem with adopted new nominal attributes {a j (n)} j=10 j=1 , n = 1, 2, . .…”

Section: New Attributes {Cmentioning

confidence: 99%

On Reduction of Data Series Dimensionality

Krawczak

Szkatuła

2014

Studies in Computational Intelligence

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In this paper we introduce a complex procedure of reducing dimensionality of multidimensional data series. The procedure consists of several steps, and each step gives a new data series representation as well as dimension reduction. The approach is based on the concept of data series aggregated envelopes, and principal components called here 'essential attributes' generated by a multilayer neural network. The essential attributes are generated by outputs of hidden layer neurons. Next, all differences of the essential attributes are treated as new attributes. The real values of the new attributes are nominalized in order to obtain a nominal representation of data series. The approach creates a nominal representation of the original data series and considerably reduces their dimension. Practical verification of the proposed approach was verified for classification and clustering of time series problems, the results are set out in different papers of the authors. Here, the short summarization confirms utilities of time series dimension reduction procedure.

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An approach to dimensionality reduction in time series

Cited by 50 publications

References 33 publications

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

On Perturbation Measure of Sets – Properties

On Reduction of Data Series Dimensionality

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