Name
Abstract | ||
|---|---|---|
Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nystrom approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S0129065712500219 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Supervised learning, dissimilarity data, nystroem approximation | Electronic data,Semi-supervised learning,Pattern recognition,Relational database,Computer science,Support vector machine,Learning vector quantization,Supervised learning,Unsupervised learning,Artificial intelligence,Time complexity,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 5 | 0129-0657 |
Citations | PageRank | References |
16 | 0.67 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrej Gisbrecht | 1 | 195 | 15.60 |
| Bassam Mokbel | 2 | 189 | 14.73 |
| Frank-Michael Schleif | 3 | 427 | 46.59 |
| Xibin Zhu | 4 | 108 | 8.72 |
| Barbara Hammer | 5 | 2383 | 181.34 |