Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning

2020 ◽  
Vol 32 (10) ◽  
pp. 2014-2025 ◽  
Author(s):  
Rong Wang ◽  
Feiping Nie ◽  
Zhen Wang ◽  
Haojie Hu ◽  
Xuelong Li
Keyword(s):  
Graph Learning ◽  
Projected Clustering

Multi-view projected clustering with graph learning

2020 ◽  
Vol 126 ◽  
pp. 335-346 ◽  
Author(s):  
Quanxue Gao ◽  
Zhizhen Wan ◽  
Ying Liang ◽  
Qianqian Wang ◽  
Yang Liu ◽  
...  
Keyword(s):  
Graph Learning ◽  
Projected Clustering

Multi-view feature selection via Nonnegative Structured Graph Learning

2020 ◽  
Vol 387 ◽  
pp. 110-122 ◽  
Author(s):  
Xiangpin Bai ◽  
Lei Zhu ◽  
Cheng Liang ◽  
Jingjing Li ◽  
Xiushan Nie ◽  
...  
Keyword(s):  
Feature Selection ◽  
Graph Learning

scholarly journals Online Discriminative Graph Learning from Multi-Class Smooth Signals

2021 ◽  
pp. 108101
Author(s):  
Seyed Saman Saboksayr ◽  
Gonzalo Mateos ◽  
Mujdat Cetin
Keyword(s):  
Graph Learning

scholarly journals Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

2021 ◽  
Author(s):  
Yun Peng ◽  
Byron Choi ◽  
Jianliang Xu
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Graph Embedding ◽  
Partial Solution ◽  
Complex Data ◽  
Learning Methods ◽  
Graph Learning ◽  
Second Stage ◽  
End To End ◽  
Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

Low-Rank Tensor Graph Learning for Multi-view Subspace Clustering

2021 ◽  
pp. 1-1
Author(s):  
Yongyong Chen ◽  
Xiaolin Xiao ◽  
Chong Peng ◽  
Guangming Lu ◽  
Yicong Zhou
Keyword(s):  
Subspace Clustering ◽  
Low Rank ◽  
Graph Learning ◽  
Rank Tensor

scholarly journals Similarity Graph Learning and Non-linear Deep Representations for Spectral Clusterings

2021 ◽  
Vol 1757 (1) ◽  
pp. 012001
Author(s):  
Ni Li ◽  
Manman Peng ◽  
Buwen Cao ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Graph Learning ◽  
Non Linear ◽  
Similarity Graph

Kernelized multi-view subspace clustering via auto-weighted graph learning

2021 ◽  
Author(s):  
Guang-Yu Zhang ◽  
Xiao-Wei Chen ◽  
Yu-Ren Zhou ◽  
Chang-Dong Wang ◽  
Dong Huang ◽  
...  
Keyword(s):  
Subspace Clustering ◽  
Weighted Graph ◽  
Graph Learning
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