A photo of coffee being dripped into a flask from a paper filter symbolising PCA Principal Component Analysis Algorithm

Principal Component Analysis Algorithm

Sometimes data can have large amounts of features, so many that further processing or inference can be hampered. When this occurs Principal Component Analysis Algorithm (PCA), an Unsupervised Learning algorithm, is used to reduce the number of features whilst retaining as much information as possible. This is Feature Engineering.

PCA has two modes: Regular and Randomised.

Regular modeRandomized mode
Data densitySparseDenser
ObservationsModerateLarge
FeaturesModeratelarge

Attributes

Problem attributeDescription
Data types and formatTabular
Learning paradigm or domainUnsupervised Learning
Problem typeFeature engineering: dimensionality reduction
Use case examplesDrop those columns from a dataset that have a weak relation with the label/target variable

Training

Training data has to be in CSV or recordIO-wrapped-protobuf format.

Model artifacts and inference

DescriptionArtifacts
Learning paradigmUnsupervised Learning
Request formatCSV, JSON, x-recordio-protobuf
ResultJSON, x-recordio-protobuf

Processing environment

Both CPU and GPU instances can be used.

Credits

Photo by Najib Kalil on Unsplash

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