Name
Abstract | ||
|---|---|---|
Recent years have witnessed great success of manifold learning methods in understanding the structure of multidimensional patterns. However, most of these methods operate in a batch mode and cannot be effectively applied when data are collected sequentially. In this paper, we propose a general incremental learning framework, capable of dealing with one or more new samples each time, for the so-called spectral embedding methods. In the proposed framework, the incremental dimensionality reduction problem reduces to an incremental eigen-problem of matrices. Furthermore, we present, using this framework as a tool, an incremental version of Hessian eigenmaps, the IHLLE method. Finally, we show several experimental results on both synthetic and real world datasets, demonstrating the efficiency and accuracy of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.patrec.2011.04.004 | Pattern Recognition Letters |
Keywords | Field | DocType |
hessian eigenmaps,manifold learning,batch mode,incremental dimensionality reduction problem,proposed algorithm,ihlle method,spectral embedding method,proposed framework,spectral embedding methods,dimensionality reduction,general incremental learning framework,incremental eigen-problem,incremental manifold,incremental learning,incremental version | Embedding,Dimensionality reduction,Pattern recognition,Matrix (mathematics),Incremental learning,Hessian matrix,Batch processing,Artificial intelligence,Nonlinear dimensionality reduction,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 10 | Pattern Recognition Letters |
Citations | PageRank | References |
14 | 0.74 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Housen Li | 1 | 34 | 4.45 |
| Hao Jiang | 2 | 111 | 18.12 |
| Roberto Barrio | 3 | 64 | 12.04 |
| Xiangke Liao | 4 | 622 | 74.79 |
| Lizhi Cheng | 5 | 290 | 34.84 |
| Su Fang | 6 | 61 | 5.73 |