Name
Title | ||
|---|---|---|
Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine |
Abstract | ||
|---|---|---|
The locally linear embedding (LLE) algorithm is an un- supervised technique recently proposed for nonlinear dimensionality re- duction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with support vec- tor machine (SVM) for classifying handwritten digits from the MNIST database. |
Year | Venue | Keywords |
|---|---|---|
2003 | ESANN | support vector machine,high dimensional data |
Field | DocType | Citations |
Clustering high-dimensional data,Embedding,Disjoint sets,MNIST database,Nonlinear system,Pattern recognition,Computer science,Support vector machine,Algorithm,Curse of dimensionality,Artificial intelligence,Machine learning | Conference | 6 |
PageRank | References | Authors |
1.02 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olga Kouropteva | 1 | 210 | 18.87 |
| Oleg Okun | 2 | 308 | 28.56 |
| Matti Pietikäinen | 3 | 14779 | 739.80 |