Combining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning
نویسندگان
چکیده
منابع مشابه
Combining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning
Introduction Complex adaptive systems (CAS) research area is trying to establish a comprehensive and general understanding of the complex world around us (Niazi and Hussain 2013). Complex systems typically involve the generation of high dimensional data and rely on effective analysis and management of such high-dimensional data. High dimensional data exists in a wide variety of real application...
متن کاملStructure Preserving Low-Rank Representation for Semi-supervised Face Recognition
Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has shown excellent performance in semi-supervised learning. In thi...
متن کاملEnhanced low-rank representation via sparse manifold adaption for semi-supervised learning
Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labele...
متن کاملSparse semi-supervised learning on low-rank kernel
Advances of modern science and engineering lead to unprecedented amount of data for information processing. Of particular interest is the semi-supervised learning, where very few training samples are available among large volumes of unlabeled data. Graph-based algorithms using Laplacian regularization have achieved state-of-the-art performance, but can induce huge memory and computational costs...
متن کاملCombining Graph Laplacians for Semi-Supervised Learning
A foundational problem in semi-supervised learning is the construction of a graph underlying the data. We propose to use a method which optimally combines a number of differently constructed graphs. For each of these graphs we associate a basic graph kernel. We then compute an optimal combined kernel. This kernel solves an extended regularization problem which requires a joint minimization over...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Complex Adaptive Systems Modeling
سال: 2016
ISSN: 2194-3206
DOI: 10.1186/s40294-016-0034-7