6533b827fe1ef96bd12875fb

RESEARCH PRODUCT

Contribution à l’apprentissage de représentation de données à base de graphes avec application à la catégorisation d’images

subject

description

Graph-based Manifold Learning algorithms are regarded as a powerful technique for feature extraction and dimensionality reduction in Pattern Recogniton, Computer Vision and Machine Learning fields. These algorithms utilize sample information contained in the item-item similarity and weighted matrix to reveal the intrinstic geometric structure of manifold. It exhibits the low dimensional structure in the high dimensional data. This motivates me to develop Graph-based Manifold Learning techniques on Pattern Recognition, specially, application to image categorization. The experimental datasets of thesis correspond to several categories of public image datasets such as face datasets, indoor and outdoor scene datasets, objects datasets and so on. Several approaches are proposed in this thesis: 1) A novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS) is proposed. We seek a non-linear and a linear representation of the data that can be suitable for generic learning tasks such as classification and clustering. Besides, a byproduct of the proposed embedding framework is the feature selection of the original features, where the estimated linear transformation matrix can be used for feature ranking and selection. 2) We investigate strategies and related algorithms to develop a joint graph-based embedding and an explicit feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, this chapter introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. 3) We propose the graph convolution based semi-supervised Embedding (GCSE). It provides a new perspective to non-linear data embedding research, and makes a link to signal processing on graph methods. The proposed method utilizes and exploits graphs in two ways. First, it deploys data smoothness over graphs. Second, its regression model is built on the joint use of the data and their graph in the sense that the regression model works with convolved data. The convolved data are obtained by feature propagation. 4) A flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models is introduced. We call this strategy an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently.