- Tytuł:
- Intrinsic dimensionality detection criterion based on Locally Linear Embedding
- Autorzy:
-
Meng, L.
Breitkopf, P. - Powiązania:
- https://bibliotekanauki.pl/articles/952945.pdf
- Data publikacji:
- 2018
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
LLE
dimensionality reduction
intrinsic dimensionality
neighborhood
preserving - Opis:
- In this work, we revisit the Locally Linear Embedding (LLE) algorithm that is widely employed in dimensionality reduction. With a particular interest to the correspondences of the nearest neighbors in the original and embedded spaces, we observe that, when prescribing low-dimensional embedding spaces, LLE remains merely a weight-preserving rather than a neighborhood-preserving algorithm. Thus, we propose a \neighborhood-preserving ratio" criterion to estimate the minimal intrinsic dimensionality required for neighborhood preservation. We validate its efficiency on sets of synthetic data, including S-curve, Swiss roll, and a dataset of grayscale images.
- Źródło:
-
Computer Science; 2018, 19 (3); 345-356
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł