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New Supervised Locally Linear Embedding for Dimensionality Reduction Using Distance Metric Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yang, Bo-Zhi Xiang, Min Zhang, Yue Ping |
| Copyright Year | 2016 |
| Abstract | Feature reduction is an important issue in pattern recognition. Lower feature dimensionality could reduce the complexity and enhance the generalization ability of classifiers. In this paper we propose a new supervised dimensionality reduction method based on Locally Linear Embedding and Distance Metric Learning. First, in order to increase the interclass separability, a linear discriminant transformation learnt from distance metric learning is used to map the original data points to a new space. Then Locally Linear Embedding is adopted to reduce the dimensionality of data points. This process extends the traditional unsupervised Locally Linear Embedding to supervised scenario in a clear and natural way. In addition, it can also be seen as a general framework for developing new supervised dimensionality reduction algorithms by utilizing corresponding unsupervised methods. Extensive classification experiments performed on some real-world and artificial datasets show that the proposed method can achieve comparable to or even better results over other state-of-the-art dimensionality reduction methods. |
| Starting Page | 449 |
| Ending Page | 459 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.14311/nnw.2016.26.026 |
| Volume Number | 26 |
| Alternate Webpage(s) | http://nnw.cz/doi/2016/2016_26_026.pdf |
| Alternate Webpage(s) | http://nnw.cz/doi/2016/NNW.2016.26.026.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
