Graph regularized 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 کاملGraph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful representation of the spectral response. Then, graph regularization is incorporated into the LRR model to capture the local structure information and the nonlinear property of the remote-sensing data. Since it is easy...
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 کاملRegularized Boost for Semi-Supervised Learning
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smo...
متن کاملSemi-Supervised Classification Based on Low Rank Representation
Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. Recently, low-rank representation has been introduced to construct a graph,...
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: Journal of Algorithms & Computational Technology
سال: 2021
ISSN: 1748-3026,1748-3026
DOI: 10.1177/17483026211013966