Name
Abstract | ||
|---|---|---|
Feature extraction provides an essential element in most machine learning methods, including supervised learning with neural networks. Linearly inseparable data distributions are often non-linearly transformed in some way to make them more linearly separable in the feature space. In this paper, we propose a method of feature extraction with a space folding model. In the proposed method, each basis vector in the m-dimensional data space is divided in the positive and negative directions to optimize it with 2m m-dimensional vectors as variables. 2m variable vectors are estimated to minimize the cross entropy of class labels and distances so that instances in the same classes are gathered closer together and those in other classes are separated farther apart. The proposed method, in which linear transformation is applied to each quadrant to collectively realize a non-linear transformation, is expected to lead to improvements in accuracy of discrimination over conventional methods of feature extraction using single linear transformations. In this paper, we have confirmed the effectiveness of the proposed method of feature extraction with a space folding model on a UCI benchmark problem. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.20965/jaciii.2011.p0662 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
machine learning, feature extraction, space folding model, space folding vector | Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 6 | 1343-0130 |
Citations | PageRank | References |
1 | 0.43 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minh Tuan Pham | 1 | 7 | 3.40 |
| Tomohiro Yoshikawa | 2 | 116 | 31.91 |
| Takeshi Furuhashi | 3 | 3 | 0.83 |
| Kanta Tachibana | 4 | 12 | 4.81 |