.png){kind=icon}
Return
Regularization parameter choice in locally linear embedding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2009.11.038.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Locally linear embedding (LLE) is a recent unsupervised learning algorithm for non-linear dimensionality reduction of high dimensional data. One advantage of this algorithm is that just two parameters are needed to be set by user: the number of nearest neighbors and a regularization parameter. The choice of the regularization parameter plays an important role in the embedding results. In this paper, an automated method for choosing this parameter is proposed. Besides, in order to objectively qualify the performance of the embedding results, a new measure of embedding quality is suggested. Our approach is experimentally verified on 9 artificial data sets and 2 real world data sets. Numerical results are compared against two methods previously found in the state of art. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Dimensionality reduction
LLE
Regularization
Embedding quality
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSaveLocal procrustes for manifold embedding: a measure of embedding quality and embedding algorithms.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAIShareSaveShareSaveShareSaveShareSave
ShareSave no more
no more