Research On GraphBased SemiSupervised Learning And Its Applications  Posted on:20121227  Degree:Doctor  Type:Dissertation  Country:China  Candidate:J Pan  Full Text:PDF  GTID:1118330371458963  Subject:Computer Science and Technology  Abstract/Summary:  PDF Full Text Request  The perception and utilization of prior knowledge is one of the most important tasks of machine learning methods. In addition to regular supervised information such as class labels and pairwise constraints, the geometry structure of the data distribution is also one of the most important parts of the prior knowledge. Recently, using graph to approach local manifold has been intensively studied, thanks to its convenient local representation, connection with other classical models like regularization techniques, kernel methods and spectral graph theory. In particular, graph based semisupervised learning has gained considerable interest by its ability to utilize both labeled data and a large quantity of unlabeled data to improve learning performance.Following discussion on the development history and existing challenges of graphbased semisupervise learning, this dissertation focus on two main topics, i.e. graph construction and graph optimization, and its applications in traditional learning tasks such as classification, clustering and dimensionality reduction. The main results and contributions of this dissertation are as follows.1,The regularization framework that respects both local geometry structure and global discriminate structure is developed, which can be extended to a lot of semisupervised classification methods by choosing different loss function and regularization term. Based on the framework, a novel approach called locality sensitive discriminate transductive learning (LSDTL) was developed. LSDTL directly incorporates the discriminative information as well as the local geometry of the data space into the regularization framework of transductive learning, so that it can explore as much underlying knowledge as possible, and maximize the margins between the data of different classes in each local manifold.2,A novel approach that utilizes prior knowledge to enhance the performance of both dimensionality reduction and data clustering was developed. The new method expands the original constraints set by knearest neighbors of the pairwise constraints, then assigned weights to each pairwise constraint by its information power, and finally finds a proper projection matrix guided by the weighted pairwise constraints. With the projection matrix, all the data were projected onto a lowdimensional manifold, so that the intraclass distance is decreased and the interclass distance is increased. In addition, a new evaluation function was introduced to enforce the kmeans cluster algorithm, which had enabled it to provide an appealing clustering performance with minimum violation of the pairwise constraints.3,A novel approach called semisupervised feature selection based on structure and constraints preserving (SCP) was presented. The SCP method takes both the pairwise constraints and the local & nonlocal structure into account, and defines a new feature selection criterion, i.e. SCP Score. The SCP Score exploits abundant unlabeled data points to learn the geometrical structure of the data space, and uses a few pairwise constraints to discover the margins of different classes. Those features that can preserve the geometrical structure and pairwise constraints information are selected.4,A novel approach based on graph random walk was presented. All the data objects are carried by agents and then mapped to the output space represented by lattices, and the spatial configuration of the agents forms a selforganizing Markov stochastic process. The Markov process finally converges to a stationary probability distribution, in which an optimal label distribution is provided.  Keywords/Search Tags:  semisupervised learning, graph learning, manifold learning, classification methods, dimensionality reduction, clustering methods, regularization techniques  PDF Full Text Request  Related items 
 
