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Unsupervised Learning

Definition
AI-generated

Unsupervised learning discovers structure in inputs without per-example labels—via clustering, low-dimensional embeddings, density modeling, or self-supervised pretext tasks—often as a precursor to downstream prediction or interpretation.

Topics
LLM and Agents
Related
Autoencoder Disentangled Representation Learning Machine Learning

Why it matters in GWAS

Unsupervised steps include PCA for population structure, UMAP or autoencoders on single-cell data, and latent models for batch correction. Latent factors may align with ancestry or hidden relatedness, which can confound later association analyses if not regressed or modeled explicitly.

Example usage

"A replication analysis checks whether assumptions tied to Unsupervised Learning remain stable across cohorts."

Autoencoder Disentangled Representation Learning Machine Learning Principal Component Analysis (PCA) Supervised Learning UMAP
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Bishop CM. (2006). Pattern Recognition and Machine Learning. Springer.
← Unique Molecular Identifier (UMI) Untranslated region (UTR) →

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