1,977 research outputs found
Structure fusion based on graph convolutional networks for semi-supervised classification
Suffering from the multi-view data diversity and complexity for
semi-supervised classification, most of existing graph convolutional networks
focus on the networks architecture construction or the salient graph structure
preservation, and ignore the the complete graph structure for semi-supervised
classification contribution. To mine the more complete distribution structure
from multi-view data with the consideration of the specificity and the
commonality, we propose structure fusion based on graph convolutional networks
(SF-GCN) for improving the performance of semi-supervised classification.
SF-GCN can not only retain the special characteristic of each view data by
spectral embedding, but also capture the common style of multi-view data by
distance metric between multi-graph structures. Suppose the linear relationship
between multi-graph structures, we can construct the optimization function of
structure fusion model by balancing the specificity loss and the commonality
loss. By solving this function, we can simultaneously obtain the fusion
spectral embedding from the multi-view data and the fusion structure as
adjacent matrix to input graph convolutional networks for semi-supervised
classification. Experiments demonstrate that the performance of SF-GCN
outperforms that of the state of the arts on three challenging datasets, which
are Cora,Citeseer and Pubmed in citation networks
Graph-based classification of multiple observation sets
We consider the problem of classification of an object given multiple
observations that possibly include different transformations. The possible
transformations of the object generally span a low-dimensional manifold in the
original signal space. We propose to take advantage of this manifold structure
for the effective classification of the object represented by the observation
set. In particular, we design a low complexity solution that is able to exploit
the properties of the data manifolds with a graph-based algorithm. Hence, we
formulate the computation of the unknown label matrix as a smoothing process on
the manifold under the constraint that all observations represent an object of
one single class. It results into a discrete optimization problem, which can be
solved by an efficient and low complexity algorithm. We demonstrate the
performance of the proposed graph-based algorithm in the classification of sets
of multiple images. Moreover, we show its high potential in video-based face
recognition, where it outperforms state-of-the-art solutions that fall short of
exploiting the manifold structure of the face image data sets.Comment: New content adde
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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