23,903 research outputs found
Multi-Source Multi-View Clustering via Discrepancy Penalty
With the advance of technology, entities can be observed in multiple views.
Multiple views containing different types of features can be used for
clustering. Although multi-view clustering has been successfully applied in
many applications, the previous methods usually assume the complete instance
mapping between different views. In many real-world applications, information
can be gathered from multiple sources, while each source can contain multiple
views, which are more cohesive for learning. The views under the same source
are usually fully mapped, but they can be very heterogeneous. Moreover, the
mappings between different sources are usually incomplete and partially
observed, which makes it more difficult to integrate all the views across
different sources. In this paper, we propose MMC (Multi-source Multi-view
Clustering), which is a framework based on collective spectral clustering with
a discrepancy penalty across sources, to tackle these challenges. MMC has
several advantages compared with other existing methods. First, MMC can deal
with incomplete mapping between sources. Second, it considers the disagreements
between sources while treating views in the same source as a cohesive set.
Third, MMC also tries to infer the instance similarities across sources to
enhance the clustering performance. Extensive experiments conducted on
real-world data demonstrate the effectiveness of the proposed approach
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Incomplete multi-view clustering (IMVC) has received increasing attention
since it is often that some views of samples are incomplete in reality. Most
existing methods learn similarity subgraphs from original incomplete multi-view
data and seek complete graphs by exploring the incomplete subgraphs of each
view for spectral clustering. However, the graphs constructed on the original
high-dimensional data may be suboptimal due to feature redundancy and noise.
Besides, previous methods generally ignored the graph noise caused by the
inter-class and intra-class structure variation during the transformation of
incomplete graphs and complete graphs. To address these problems, we propose a
novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD)
for IMVC. Specifically, to alleviate the influence of redundant features and
noise in high-dimensional data, JPLTD introduces an orthogonal projection
matrix to project the high-dimensional features into a lower-dimensional space
for compact feature learning.Meanwhile, based on the lower-dimensional space,
the similarity graphs corresponding to instances of different views are
learned, and JPLTD stacks these graphs into a third-order low-rank tensor to
explore the high-order correlations across different views. We further consider
the graph noise of projected data caused by missing samples and use a
tensor-decomposition based graph filter for robust clustering.JPLTD decomposes
the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic
tensor models the true data similarities. An effective optimization algorithm
is adopted to solve the JPLTD model. Comprehensive experiments on several
benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art
methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
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
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