In this paper, extending the GE framework [7] so as to include subclass information, we propose a novel Subclass Marginal. Fisher Analysis (SMFA) algorithm for ...

In this paper, based on the SGE framework, a novel Subclass Marginal Fisher Analysis (SMFA) algorithm for supervised dimensionality reduction has been proposed.

Abstract—Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently,.

Although MFA surpasses the above distribution limitations, it fails to model potential subclass structure that might lie within the several classes of the data.

Apr 14, 2015 · Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains.

This repository contains Matlab code that performs dimensionality reduction and classification using the Subclass Graph Embedding methodology presented in ...

In this paper a novel DR algorithm, which uses subclass discriminant information, called Subclass Marginal Fisher Analysis (SMFA) has been proposed.

Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains.

In this paper, we propose the use of Subclass Marginal Fisher Analysis (SMFA) in order to overcome such problems. SMFA has the power to effectively learn ...

Subclass Marginal Fisher Analysis · Anastasios Maronidis. 2015, 2015 IEEE Symposium Series on Computational Intelligence. Download Free PDF View PDF. Free PDF.