Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used
for different classification tasks, these methods have some shortcomings.
The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity.
Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the `2;1 constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

Linear Embedding by Joint Robust Discriminant Analysis and Inter-class Sparsity

The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multiclass
classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Interclass sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and
robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix.
The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.

Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity

**Session Chairs**: prof. Yair Weiss, prof. Gian Luca Foresti

OS T1.3: Clustering for Pattern Analysis

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Ahmad Khoder

3

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Linear Embedding by Joint Robust Discriminant Analysis and Inter-class Sparsity

Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity

Linear Embedding by Joint Robust Discriminant Analysis and Inter-class Sparsity

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