Name
Abstract | ||
|---|---|---|
When the number of labeled data is not sufficient, Semi-Supervised Learning (SSL) methods utilize unlabeled data to enhance classification. Recently, many SSL methods have been developed based on the manifold assumption in a batch mode. However, when data arrive sequentially and in large quantities, both computation and storage limitations become a bottleneck. In this paper, we present a new semisupervised coarse graining (CG) algorithm to reduce the required number of data points for preserving the manifold structure. First, an equivalent formulation of Label Propagation (LP) is derived. Then a novel spectral view of the Harmonic Solution (HS) is proposed. Finally an algorithm to reduce the number of data points while preserving the manifold structure is provided and a theoretical analysis on preservation of the LP properties is presented. Experimental results on real world datasets show that the proposed method outperforms the state of the art coarse graining algorithm in different settings. |
Year | Venue | Keywords |
|---|---|---|
2011 | ECML/PKDD (1) | new semisupervised coarse graining,online semi-supervised learning,data point,manifold structure,manifold coarse graining,required number,manifold assumption,art coarse graining algorithm,unlabeled data,SSL method,proposed method,LP property |
Field | DocType | Volume |
Data point,Bottleneck,Semi-supervised learning,Algorithm,Theoretical computer science,Manifold alignment,Batch processing,Granularity,Mathematics,Manifold,Computation | Conference | 6911 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.46 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehrdad Farajtabar | 1 | 230 | 20.70 |
| Amirreza Shaban | 2 | 48 | 5.60 |
| Hamid Reza Rabiee | 3 | 79 | 11.48 |
| Mohammad Hossein Rohban | 4 | 35 | 3.35 |