Definition
High-dimensional refers to datasets or vector spaces containing a vast number of attributes or features. In AI, this is common in text embeddings, image pixels, or gene expression data. While rich in information, high dimensionality can cause the ‘curse of dimensionality,’ where data becomes sparse, distances between points lose meaning, and models require significantly more data and computational power to learn effectively.
Summary
Describes data spaces with a large number of features or dimensions, often leading to sparsity and computational challenges.
Key Concepts
- Curse of dimensionality
- Feature space
- Sparsity
- Dimensionality reduction
Use Cases
- Natural Language Processing (word embeddings)
- Computer vision (pixel arrays)
- Recommendation systems