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principal component analysis
This term is a technical designation used primarily in statistics, data science, and machine learning. It describes a specific mathematical process for simplifying complex datasets.
Because it refers to a singular, defined methodology, it is treated as an uncountable noun in professional discourse. In practice, the term is frequently abbreviated as PCA. While the individual components resulting from the analysis are countable, the analysis itself is a conceptual procedure and does not typically take a plural form.
Meanings
A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Examples
We used principal component analysis to reduce the dimensionality of the dataset.
I think we should apply principal component analysis to identify the most important variables.
Principal component analysis allows us to visualize high dimensional data in a two dimensional plot.
Principal component analysis allows us to visualize high dimensional data in a two dimensional plot.
The researcher performed a principal component analysis to simplify the complex genetic data.
Does this model rely on principal component analysis for feature extraction?
By using principal component analysis, the team managed to eliminate redundant features from the model.
By using principal component analysis, the team managed to eliminate redundant features from the model.
The first few components of the principal component analysis captured most of the variance.
I wonder if principal component analysis is the best approach for this specific type of noise.