Truncated Graph-Regularized Low Rank Representation for Link Prediction
نویسندگان
چکیده
منابع مشابه
Graph 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 کاملGraph Spectra Regression with Low-Rank Approximation for Dynamic Graph Link Prediction∗
We propose a temporal regression model for dynamic graph link prediction problem, under spectral graph theory and low rank approximation for the graph Laplacian matrix. Link prediction is important in large-scale graphs including social networks, biological networks, power grid, etc. Most of these graphs have different characteristics such as degree distribution, due to their underlying samplin...
متن کاملGene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the origin...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملLaplacian regularized low rank subspace clustering
The problem of fitting a union of subspaces to a collection of data points drawn from multiple subspaces is considered in this paper. In the traditional low rank representation model, the dictionary used to represent the data points is chosen as the data points themselves and thus the dictionary is corrupted with noise. This problem is solved in the low rank subspace clustering model which deco...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2909757