A PCA-based similarity measure for multivariate time series

Yang, Kiyoung; Shahabi, Cyrus

doi:10.1145/1032604.1032616

Cited by 217 publications

(162 citation statements)

References 27 publications

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“…PCAsimilarity(): The Krzanowski correlation compares only the subspace shared by roughly the first half of the principal components, but does not consider the amount of variation each population has in these directions of the morphological space 60 . In order to take the variation into account, we can add the eigenvalue associated with each principal component into the calculation, effectively weighting each correlation by the variance in the associated directions.…”

Section: Matrix Comparisonmentioning

confidence: 99%

EvolQG - An R package for evolutionary quantitative genetics

Melo¹,

Garcia²,

Hubbe³

et al. 2016

F1000Res

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We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.

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Section: Matrix Comparisonmentioning

confidence: 99%

EvolQG - An R package for evolutionary quantitative genetics

Melo¹,

Garcia²,

Hubbe³

et al. 2016

F1000Res

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“…Instead of comparing two MTS data represented in matrices elementby-element, Eros computes the similarity between two MTS data by measuring how similarity two corresponding principal components (PCs) are using the aggregated eigenvalues as weight. For Eros, we performed modified leave-oneout kNN search as in [33]. For simplicity, we chose 10 for maxr.…”

Section: Methodsmentioning

confidence: 99%

“…Even though our approach is general, to focus the discussion we describe our approach within the context of our previously proposed techniques for MTS variable-subsetselection and similarity search. The performances depending on the stationarity of the data set have been compared in terms of the classification accuracy using Corona [34] and the precision/recall using Eros [33].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…To the best of our knowledge, there has been no attempt to index data sets with more than 100000 dimensions/features 1 . Hence, instead of using this high dimensional MTS data set as is, a number of techniques have been proposed to represent the MTS data set concisely for data mining processes, such as classification and similarity search [24,22,30,33,35]. For example, in [22], an MTS item that contains an EEG signal with 39 channels is decomposed into multiple univariate time series.…”

Section: Introductionmentioning

confidence: 99%

“…For MTS analysis, e.g., similarity search and feature subset selection, it has been empirically shown that the correlation information among the variables plays an important role, and the correlation-based techniques, such as PCA, perform well [30,24,33]. In [34], each MTS item is represented with the upper triangle elements of the correlation coefficient matrix.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis

Yang

Shahabi

Fifth IEEE International Conference on Data Mining (ICDM'05)

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Secure principal component analysis in multiple distributed nodes

Won

Kim

Lee

et al. 2016

Security Comm Networks

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Privacy preservation becomes an important issue in recent big data analysis, and many secure multiparty computations have been proposed for the purpose of privacy preservation in the environment of distributed nodes. As a secure multiparty computations of principal component analysis (PCA), in this paper, we propose S‐PCA, which compute PCA securely among the distributed nodes. PCA is widely used in many applications including time‐series analysis, text mining, and image compression. In general, we compute PCA after concentrating all data in a single server, but this approach discloses data privacy of each node. In contrast, the proposed S‐PCA computes PCA without disclosing the sensitive data of individual nodes. In S‐PCA, the nodes share non‐sensitive mean vectors first and compute covariance matrices and PCA securely using the shared mean vectors. In this paper, we formally prove the correctness and secureness of S‐PCA and apply it to an application of secure similar document detection. Experimental results show that the performance of S‐PCA is slightly worse than that of PCA due to guarantee of secureness, but it significantly improves the performance of secure similar document detection by up to two orders of magnitudes. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

A PCA-based similarity measure for multivariate time series

Cited by 217 publications

References 27 publications

EvolQG - An R package for evolutionary quantitative genetics

EvolQG - An R package for evolutionary quantitative genetics

On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis

Secure principal component analysis in multiple distributed nodes

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