A large image-feature table can be difficult to explore. Principal component analysis offers a lower-dimensional representation, but its axes are not automatically meaningful product categories.
Scikit-learn describes PCA as projecting data onto components that account for variation. Scikit-learn — decomposition.
Inspect what dominates
For an archive, lighting and background may contribute substantial variation. A clear two-dimensional plot could therefore separate photography conditions more strongly than character families. Examine actual examples before naming the axes.
Protect the purpose
Choose the representation around the downstream task and evaluate what information it loses. Compression that makes a plot easier to view may remove a subtle detail important to retrieval. Keep the original features and product identifiers available for comparison.
Source reviewed 25 October 2026. Interpretation and original diagrams: PLUSHIP / Qunhe. Examples are illustrative.
Related reading
An Unusual Image Is Not Automatically a Defective Product
Rotated Boxes Can Describe the Orientation of Flat Components
Instance Masks Separate Individual Objects in a Product Photograph