Abstract
Dimensionality reduction is one of the basic operations in the toolbox of data analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by representing them with a smaller set of more condensed variables. Another reason for reducing the dimensionality is to reduce computational load in further processing. A third reason is visualization.
| Original language | English |
|---|---|
| Article number | 5714379 |
| Pages (from-to) | 100-104 |
| Number of pages | 5 |
| Journal | IEEE Signal Processing Magazine |
| Volume | 28 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2011 |
| Publication type | A1 Journal article-refereed |
Keywords
- Data models
- Data visualization
- Information retrieval
- Machine learning
- Manifolds
- Probabilistic logic
- Visualization
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