Dimensionality reduction for data visualization
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Artikkeli | 5714379 |
| Sivut | 100-104 |
| Sivumäärä | 5 |
| Julkaisu | IEEE Signal Processing Magazine |
| Vuosikerta | 28 |
| Numero | 2 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - maaliskuuta 2011 |
| OKM-julkaisutyyppi | A1 Alkuperäisartikkeli |
Tiivistelmä
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.