Abstract
Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, Discriminant Analysis (DA) methods, which use subclass information for the discrimination between the data classes, have attracted much attention. As DA
methods are strongly dependent on the underlying distribution of the data, techniques whose functionality is based on neighbourhood information among the
data samples have emerged. For instance, based on the Graph Embedding (GE) framework, which is a platform for developing novel DR methods, Marginal
Fisher Analysis (MFA) has been proposed. Although MFA surpasses the above distribution limitations, it fails to model potential subclass structure that might
lie within the several classes of the data. In this paper, motivated by the need to alleviate the above shortcomings, we propose a novel DR technique, called Subclass Marginal Fisher Analysis (SMFA), which combines the strength of subclass DA methods with the versatility of MFA. The new method is built by extending the GE framework so as to include subclass information. Through a series of experiments on various real-world datasets, it is shown that SMFA outperforms in most of the cases the state-of-the-art demonstrating the potential
of exploiting subclass neighbourhood information in the DR process.
methods are strongly dependent on the underlying distribution of the data, techniques whose functionality is based on neighbourhood information among the
data samples have emerged. For instance, based on the Graph Embedding (GE) framework, which is a platform for developing novel DR methods, Marginal
Fisher Analysis (MFA) has been proposed. Although MFA surpasses the above distribution limitations, it fails to model potential subclass structure that might
lie within the several classes of the data. In this paper, motivated by the need to alleviate the above shortcomings, we propose a novel DR technique, called Subclass Marginal Fisher Analysis (SMFA), which combines the strength of subclass DA methods with the versatility of MFA. The new method is built by extending the GE framework so as to include subclass information. Through a series of experiments on various real-world datasets, it is shown that SMFA outperforms in most of the cases the state-of-the-art demonstrating the potential
of exploiting subclass neighbourhood information in the DR process.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015) |
| Subtitle of host publication | Proceedings of a meeting held 7-10 December 2015, Cape Town, South Africa |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1391-1398 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781479975600 |
| ISBN (Print) | 9781479975617 |
| DOIs | |
| Publication status | Published - Apr 2016 |
| Event | 2015 IEEE Symposium Series on Computational Intelligence - Cape Town, South Africa Duration: 7 Dec 2015 → 10 Dec 2015 |
Conference
| Conference | 2015 IEEE Symposium Series on Computational Intelligence |
|---|---|
| Abbreviated title | SSCI 2015 |
| Country/Territory | South Africa |
| City | Cape Town |
| Period | 7/12/15 → 10/12/15 |