What is PCA?

Principal Component Analysis (PCA) is 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. PCA is used for dimensionality reduction, feature extraction, and data visualization, especially when dealing with high-dimensional data.

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What other technologies are related to PCA?

PCA Competitor Technologies

CCA

Canonical Correlation Analysis (CCA) is a related dimensionality reduction technique that aims to find correlations between two sets of variables.

t-SNE

t-distributed Stochastic Neighbor Embedding (t-SNE) is another dimensionality reduction technique, often used for visualization.

tSNE

No summary available

t-distributed Stochastic Neighbor Embedding (t-SNE) is another dimensionality reduction technique, often used for visualization.

UMAP

Uniform Manifold Approximation and Projection (UMAP) is a more modern dimensionality reduction technique that can be used as an alternative to PCA or t-SNE.

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PCA Complementary Technologies

SVD

Singular Value Decomposition (SVD) is the mathematical foundation for PCA. PCA is typically implemented using SVD.

SVM

Support Vector Machines can benefit from PCA as a pre-processing step to reduce dimensionality and improve performance.

DBSCAN

DBSCAN is a clustering algorithm. PCA can be used as a pre-processing step to reduce the dimensionality of the data before applying DBSCAN.

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Which job functions commonly mention PCA?

Data Scientist

0.2% of all Data Scientist jobs mention PCA

View 453 jobs on Sumble

SAP Administrator

0.1% of all SAP Administrator jobs mention PCA

View 368 jobs on Sumble

Machine Learning

0.1% of all Machine Learning jobs mention PCA

View 120 jobs on Sumble

Data, Analytics & Machine Learning

715 Data, Analytics & Machine Learning jobs mention PCA

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Information Technology

442 Information Technology jobs mention PCA

View 442 jobs on Sumble

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Which organizations are mentioning PCA?

Oracle

Scientific and Technical Services

Intel Corporation

Manufacturing

McDonald's

Accommodation and Food Services

GMR Group

Construction

CNRS

Public Administration

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