The new method of Extraction and Analysis of Non-linear Features for face recognition

In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e. Locality Preserving Analysis. In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in the previous algorithms and removes the disadvantages of ELPDA methods (local neighborhood separator analysis) using the Scatter matrix in the form of a between-class scatter that this matrix introduces and displayes the nearest neighbors to K of the outer class by the samples. In addition, another advantage of EANF is high-speed in the face recognition through miniaturizing the size of feature matrix by NLPCA (Non-Linear Locality Preserving Analysis). Finally, the results of tests on FERET Dataset show the impact of the proposed method on the face recognition.DOI:http://dx.doi.org/10.11591/ijece.v2i6.177

Evolutionary discriminant feature extraction with application to face recognition

Author name used in this publication: David Zhang2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Contribution to supervised representation learning: algorithms and applications.

278 p.In this thesis, we focus on supervised learning methods for pattern categorization. In this context, itremains a major challenge to establish efficient relationships between the discriminant properties of theextracted features and the inter-class sparsity structure.Our first attempt to address this problem was to develop a method called "Robust Discriminant Analysiswith Feature Selection and Inter-class Sparsity" (RDA_FSIS). This method performs feature selectionand extraction simultaneously. The targeted projection transformation focuses on the most discriminativeoriginal features while guaranteeing that the extracted (or transformed) features belonging to the sameclass share a common sparse structure, which contributes to small intra-class distances.In a further study on this approach, some improvements have been introduced in terms of theoptimization criterion and the applied optimization process. In fact, we proposed an improved version ofthe original RDA_FSIS called "Enhanced Discriminant Analysis with Class Sparsity using GradientMethod" (EDA_CS). The basic improvement is twofold: on the first hand, in the alternatingoptimization, we update the linear transformation and tune it with the gradient descent method, resultingin a more efficient and less complex solution than the closed form adopted in RDA_FSIS.On the other hand, the method could be used as a fine-tuning technique for many feature extractionmethods. The main feature of this approach lies in the fact that it is a gradient descent based refinementapplied to a closed form solution. This makes it suitable for combining several extraction methods andcan thus improve the performance of the classification process.In accordance with the above methods, we proposed a hybrid linear feature extraction scheme called"feature extraction using gradient descent with hybrid initialization" (FE_GD_HI). This method, basedon a unified criterion, was able to take advantage of several powerful linear discriminant methods. Thelinear transformation is computed using a descent gradient method. The strength of this approach is thatit is generic in the sense that it allows fine tuning of the hybrid solution provided by different methods.Finally, we proposed a new efficient ensemble learning approach that aims to estimate an improved datarepresentation. The proposed method is called "ICS Based Ensemble Learning for Image Classification"(EM_ICS). Instead of using multiple classifiers on the transformed features, we aim to estimate multipleextracted feature subsets. These were obtained by multiple learned linear embeddings. Multiple featuresubsets were used to estimate the transformations, which were ranked using multiple feature selectiontechniques. The derived extracted feature subsets were concatenated into a single data representationvector with strong discriminative properties.Experiments conducted on various benchmark datasets ranging from face images, handwritten digitimages, object images to text datasets showed promising results that outperformed the existing state-ofthe-art and competing methods

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Sélection de variables pour l’analyse des données semi-supervisées dans les systèmes d’Information décisionnels

Feature selection is an important task in data mining and machine learning processes. This task is well known in both supervised and unsupervised contexts. The semi-supervised feature selection is still under development and far from being mature. In general, machine learning has been well developed in order to deal with partially-labeled data. Thus, feature selection has obtained special importance in the semi-supervised context. It became more adapted with the real world applications where labeling process is costly to obtain. In this thesis, we present a literature review on semi-supervised feature selection, with regard to supervised and unsupervised contexts. The goal is to show the importance of compromising between the structure from unlabeled part of data, and the background information from their labeled part. In particular, we are interested in the so-called «small labeled-sample problem» where the difference between both data parts is very important. In order to deal with the problem of semi-supervised feature selection, we propose two groups of approaches. The first group is of «Filter» type, in which, we propose some algorithms which evaluate the relevance of features by a scoring function. In our case, this function is based on spectral-graph theory and the integration of pairwise constraints which can be extracted from the data in hand. The second group of methods is of «Embedded» type, where feature selection becomes an internal function integrated in the learning process. In order to realize embedded feature selection, we propose algorithms based on feature weighting. The proposed methods rely on constrained clustering. In this sense, we propose two visions, (1) a global vision, based on relaxed satisfaction of pairwise constraints. This is done by integrating the constraints in the objective function of the proposed clustering model; and (2) a second vision, which is local and based on strict control of constraint violation. Both approaches evaluate the relevance of features by weights which are learned during the construction of the clustering model. In addition to the main task which is feature selection, we are interested in redundancy elimination. In order to tackle this problem, we propose a novel algorithm based on combining the mutual information with maximum spanning tree-based algorithm. We construct this tree from the relevant features in order to optimize the number of these selected features at the end. Finally, all proposed methods in this thesis are analyzed and their complexities are studied. Furthermore, they are validated on high-dimensional data versus other representative methods in the literature.La sélection de variables est une tâche primordiale en fouille de données et apprentissage automatique. Il s’agit d’une problématique très bien connue par les deux communautés dans les contextes, supervisé et non-supervisé. Le contexte semi-supervisé est relativement récent et les travaux sont embryonnaires. Récemment, l’apprentissage automatique a bien été développé à partir des données partiellement labélisées. La sélection de variables est donc devenue plus importante dans le contexte semi-supervisé et plus adaptée aux applications réelles, où l’étiquetage des données est devenu plus couteux et difficile à obtenir. Dans cette thèse, nous présentons une étude centrée sur l’état de l’art du domaine de la sélection de variable en s’appuyant sur les méthodes qui opèrent en mode semi-supervisé par rapport à celles des deux contextes, supervisé et non-supervisé. Il s’agit de montrer le bon compromis entre la structure géométrique de la partie non labélisée des données et l’information supervisée de leur partie labélisée. Nous nous sommes particulièrement intéressés au «small labeled-sample problem» où l’écart est très important entre les deux parties qui constituent les données. Pour la sélection de variables dans ce contexte semi-supervisé, nous proposons deux familles d’approches en deux grandes parties. La première famille est de type «Filtre» avec une série d’algorithmes qui évaluent la pertinence d’une variable par une fonction de score. Dans notre cas, cette fonction est basée sur la théorie spectrale de graphe et l’intégration de contraintes qui peuvent être extraites à partir des données en question. La deuxième famille d’approches est de type «Embedded» où la sélection de variable est intrinsèquement liée à un modèle d’apprentissage. Pour ce faire, nous proposons des algorithmes à base de pondération de variables dans un paradigme de classification automatique sous contraintes. Deux visions sont développées à cet effet, (1) une vision globale en se basant sur la satisfaction relaxée des contraintes intégrées directement dans la fonction objective du modèle proposé ; et (2) une deuxième vision, qui est locale et basée sur le contrôle stricte de violation de ces dites contraintes. Les deux approches évaluent la pertinence des variables par des poids appris en cours de la construction du modèle de classification. En outre de cette tâche principale de sélection de variables, nous nous intéressons au traitement de la redondance. Pour traiter ce problème, nous proposons une méthode originale combinant l’information mutuelle et un algorithme de recherche d’arbre couvrant construit à partir de variables pertinentes en vue de l’optimisation de leur nombre au final. Finalement, toutes les approches développées dans le cadre de cette thèse sont étudiées en termes de leur complexité algorithmique d’une part et sont validés sur des données de très grande dimension face et des méthodes connues dans la littérature d’autre part

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum kernel SVM and the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE.Comment: 18 pages, 8 tables, 4 figures, format deviating from plos one submission format requirements for aesthetic reason

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

Random Projection in Deep Neural Networks

This work investigates the ways in which deep learning methods can benefit from random projection (RP), a classic linear dimensionality reduction method. We focus on two areas where, as we have found, employing RP techniques can improve deep models: training neural networks on high-dimensional data and initialization of network parameters. Training deep neural networks (DNNs) on sparse, high-dimensional data with no exploitable structure implies a network architecture with an input layer that has a huge number of weights, which often makes training infeasible. We show that this problem can be solved by prepending the network with an input layer whose weights are initialized with an RP matrix. We propose several modifications to the network architecture and training regime that makes it possible to efficiently train DNNs with learnable RP layer on data with as many as tens of millions of input features and training examples. In comparison to the state-of-the-art methods, neural networks with RP layer achieve competitive performance or improve the results on several extremely high-dimensional real-world datasets. The second area where the application of RP techniques can be beneficial for training deep models is weight initialization. Setting the initial weights in DNNs to elements of various RP matrices enabled us to train residual deep networks to higher levels of performance