7,928 research outputs found
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years
owing to its capability and flexibility in modeling complex graph structure
data. Among all graph learning methods, hypergraph learning is a technique for
exploring the implicit higher-order correlations when training the embedding
space of the graph. In this paper, we propose a hypergraph learning framework
named LFH that is capable of dynamic hyperedge construction and attentive
embedding update utilizing the heterogeneity attributes of the graph.
Specifically, in our framework, the high-quality features are first generated
by the pairwise fusion strategy that utilizes explicit graph structure
information when generating initial node embedding. Afterwards, a hypergraph is
constructed through the dynamic grouping of implicit hyperedges, followed by
the type-specific hypergraph learning process. To evaluate the effectiveness of
our proposed framework, we conduct comprehensive experiments on several popular
datasets with eleven state-of-the-art models on both node classification and
link prediction tasks, which fall into categories of homogeneous pairwise graph
learning, heterogeneous pairwise graph learning, and hypergraph learning. The
experiment results demonstrate a significant performance gain (average 12.5% in
node classification and 13.3% in link prediction) compared with recent
state-of-the-art methods
End-to-End Entity Resolution for Big Data: A Survey
One of the most important tasks for improving data quality and the
reliability of data analytics results is Entity Resolution (ER). ER aims to
identify different descriptions that refer to the same real-world entity, and
remains a challenging problem. While previous works have studied specific
aspects of ER (and mostly in traditional settings), in this survey, we provide
for the first time an end-to-end view of modern ER workflows, and of the novel
aspects of entity indexing and matching methods in order to cope with more than
one of the Big Data characteristics simultaneously. We present the basic
concepts, processing steps and execution strategies that have been proposed by
different communities, i.e., database, semantic Web and machine learning, in
order to cope with the loose structuredness, extreme diversity, high speed and
large scale of entity descriptions used by real-world applications. Finally, we
provide a synthetic discussion of the existing approaches, and conclude with a
detailed presentation of open research directions
Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life
applications because it benefits from the detailed spectral information
contained in each pixel. Notably, the complex characteristics i.e., the
nonlinear relation among the captured spectral information and the
corresponding object of HSI data make accurate classification challenging for
traditional methods. In the last few years, Deep Learning (DL) has been
substantiated as a powerful feature extractor that effectively addresses the
nonlinear problems that appeared in a number of computer vision tasks. This
prompts the deployment of DL for HSI classification (HSIC) which revealed good
performance. This survey enlists a systematic overview of DL for HSIC and
compared state-of-the-art strategies of the said topic. Primarily, we will
encapsulate the main challenges of traditional machine learning for HSIC and
then we will acquaint the superiority of DL to address these problems. This
survey breakdown the state-of-the-art DL frameworks into spectral-features,
spatial-features, and together spatial-spectral features to systematically
analyze the achievements (future research directions as well) of these
frameworks for HSIC. Moreover, we will consider the fact that DL requires a
large number of labeled training examples whereas acquiring such a number for
HSIC is challenging in terms of time and cost. Therefore, this survey discusses
some strategies to improve the generalization performance of DL strategies
which can provide some future guidelines
- …