Abstract
Together, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.
Keywords
Principal component analysisComputer scienceRobustness (evolution)Noise (video)Sample size determinationDesign of experimentsData miningData MatrixOffset (computer science)Pattern recognition (psychology)Multivariate statisticsStatisticsArtificial intelligenceMathematicsMachine learningBiology
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Publication Info
- Year
- 2015
- Type
- article
- Volume
- 16
- Issue
- S18
- Pages
- S7-S7
- Citations
- 91
- Access
- Closed
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Cite This
Tomokazu Konishi
(2015).
Principal component analysis for designed experiments.
BMC Bioinformatics
, 16
(S18)
, S7-S7.
https://doi.org/10.1186/1471-2105-16-s18-s7
Identifiers
- DOI
- 10.1186/1471-2105-16-s18-s7