196,405 research outputs found
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
View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data
AbstractEffectively utilizing incomplete multi-modality data for the diagnosis of Alzheimer's disease (AD) and its prodrome (i.e., mild cognitive impairment, MCI) remains an active area of research. Several multi-view learning methods have been recently developed for AD/MCI diagnosis by using incomplete multi-modality data, with each view corresponding to a specific modality or a combination of several modalities. However, existing methods usually ignore the underlying coherence among views, which may lead to sub-optimal learning performance. In this paper, we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among views. Specifically, we first divide the original data into several views based on the availability of different modalities and then construct a hypergraph in each view space based on sparse representation. A view-aligned hypergraph classification (VAHC) model is then proposed, by using a view-aligned regularizer to capture coherence among views. We further assemble the class probability scores generated from VAHC, via a multi-view label fusion method for making a final classification decision. We evaluate our method on the baseline ADNI-1 database with 807 subjects and three modalities (i.e., MRI, PET, and CSF). Experimental results demonstrate that our method outperforms state-of-the-art methods that use incomplete multi-modality data for AD/MCI diagnosis
Localized Sparse Incomplete Multi-view Clustering
Incomplete multi-view clustering, which aims to solve the clustering problem
on the incomplete multi-view data with partial view missing, has received more
and more attention in recent years. Although numerous methods have been
developed, most of the methods either cannot flexibly handle the incomplete
multi-view data with arbitrary missing views or do not consider the negative
factor of information imbalance among views. Moreover, some methods do not
fully explore the local structure of all incomplete views. To tackle these
problems, this paper proposes a simple but effective method, named localized
sparse incomplete multi-view clustering (LSIMVC). Different from the existing
methods, LSIMVC intends to learn a sparse and structured consensus latent
representation from the incomplete multi-view data by optimizing a sparse
regularized and novel graph embedded multi-view matrix factorization model.
Specifically, in such a novel model based on the matrix factorization, a l1
norm based sparse constraint is introduced to obtain the sparse low-dimensional
individual representations and the sparse consensus representation. Moreover, a
novel local graph embedding term is introduced to learn the structured
consensus representation. Different from the existing works, our local graph
embedding term aggregates the graph embedding task and consensus representation
learning task into a concise term. Furthermore, to reduce the imbalance factor
of incomplete multi-view learning, an adaptive weighted learning scheme is
introduced to LSIMVC. Finally, an efficient optimization strategy is given to
solve the optimization problem of our proposed model. Comprehensive
experimental results performed on six incomplete multi-view databases verify
that the performance of our LSIMVC is superior to the state-of-the-art IMC
approaches. The code is available in https://github.com/justsmart/LSIMVC.Comment: Published in IEEE Transactions on Multimedia (TMM). The code is
available at Github https://github.com/justsmart/LSIMV
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
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Incomplete multi-view clustering is a hot and emerging topic. It is well
known that unavoidable data incompleteness greatly weakens the effective
information of multi-view data. To date, existing incomplete multi-view
clustering methods usually bypass unavailable views according to prior missing
information, which is considered as a second-best scheme based on evasion.
Other methods that attempt to recover missing information are mostly applicable
to specific two-view datasets. To handle these problems, in this paper, we
propose an information recovery-driven deep incomplete multi-view clustering
network, termed as RecFormer. Concretely, a two-stage autoencoder network with
the self-attention structure is built to synchronously extract high-level
semantic representations of multiple views and recover the missing data.
Besides, we develop a recurrent graph reconstruction mechanism that cleverly
leverages the restored views to promote the representation learning and the
further data reconstruction. Visualization of recovery results are given and
sufficient experimental results confirm that our RecFormer has obvious
advantages over other top methods.Comment: Accepted by TNNLS 2023. Please contact me if you have any questions:
[email protected]. The code is available at:
https://github.com/justsmart/RecForme
Scalable Incomplete Multi-View Clustering with Structure Alignment
The success of existing multi-view clustering (MVC) relies on the assumption
that all views are complete. However, samples are usually partially available
due to data corruption or sensor malfunction, which raises the research of
incomplete multi-view clustering (IMVC). Although several anchor-based IMVC
methods have been proposed to process the large-scale incomplete data, they
still suffer from the following drawbacks: i) Most existing approaches neglect
the inter-view discrepancy and enforce cross-view representation to be
consistent, which would corrupt the representation capability of the model; ii)
Due to the samples disparity between different views, the learned anchor might
be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete
data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades
clustering performance. To tackle these issues, we propose a novel incomplete
anchor graph learning framework termed Scalable Incomplete Multi-View
Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the
view-specific anchor graph to capture the complementary information from
different views. In order to solve the AUP-ID, we propose a novel structure
alignment module to refine the cross-view anchor correspondence. Meanwhile, the
anchor graph construction and alignment are jointly optimized in our unified
framework to enhance clustering quality. Through anchor graph construction
instead of full graphs, the time and space complexity of the proposed SIMVC-SA
is proven to be linearly correlated with the number of samples. Extensive
experiments on seven incomplete benchmark datasets demonstrate the
effectiveness and efficiency of our proposed method. Our code is publicly
available at https://github.com/wy1019/SIMVC-SA
Self-Learning Symmetric Multi-view Probabilistic Clustering
Multi-view Clustering (MVC) has achieved significant progress, with many
efforts dedicated to learn knowledge from multiple views. However, most
existing methods are either not applicable or require additional steps for
incomplete MVC. Such a limitation results in poor-quality clustering
performance and poor missing view adaptation. Besides, noise or outliers might
significantly degrade the overall clustering performance, which are not handled
well by most existing methods. In this paper, we propose a novel unified
framework for incomplete and complete MVC named self-learning symmetric
multi-view probabilistic clustering (SLS-MPC). SLS-MPC proposes a novel
symmetric multi-view probability estimation and equivalently transforms
multi-view pairwise posterior matching probability into composition of each
view's individual distribution, which tolerates data missing and might extend
to any number of views. Then, SLS-MPC proposes a novel self-learning
probability function without any prior knowledge and hyper-parameters to learn
each view's individual distribution. Next, graph-context-aware refinement with
path propagation and co-neighbor propagation is used to refine pairwise
probability, which alleviates the impact of noise and outliers. Finally,
SLS-MPC proposes a probabilistic clustering algorithm to adjust clustering
assignments by maximizing the joint probability iteratively without category
information. Extensive experiments on multiple benchmarks show that SLS-MPC
outperforms previous state-of-the-art methods
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