Name
Abstract | ||
|---|---|---|
Often in practice one deals with a large amount of unlabeled data, while the fraction of labeled data points will typically be small. Therefore one prefers to apply a semi-supervised algorithm, which uses both labeled and unlabeled data points in the learning process, to have a better performance. Considering the large amount of unlabeled data, making a semi-supervised algorithm scalable is an important task. In this paper we adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it scalable by means of two different approaches. The first one is based on the Nyström approximation method which provides a finite dimensional feature map that can then be used to solve the optimization problem in the primal. The second approach is based on the reduced kernel technique that solves the problem in the dual by reducing the dimensionality of the kernel matrix to a rectangular kernel. Experimental results demonstrate the scalability and efficiency of the proposed approaches on real datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889824 | Neural Networks |
Keywords | Field | DocType |
approximation theory,learning (artificial intelligence),matrix algebra,optimisation,pattern clustering,KSC based model,MSS-KSC,Nyström approximation method,finite dimensional feature map,kernel matrix dimensionality reduction,kernel spectral clustering,large scale semisupervised learning,multiclass semisupervised KSC based algorithm,optimization problem,rectangular kernel,reduced kernel technique,unlabeled data points | Semi-supervised learning,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Tree kernel,Polynomial kernel,Artificial intelligence,String kernel,Kernel method,Variable kernel density estimation,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 7 | 0.45 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siamak Mehrkanoon | 1 | 103 | 11.90 |
| Johan A. K. Suykens | 2 | 635 | 53.51 |