418 research outputs found
Marginalized Multiview Ensemble Clustering
Multiview clustering (MVC), which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single-view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M 2 VEC) method in this paper. Specifically, we solve MVC in an EC way, which generates BPs for each view individually and seeks for a consensus one. By this means, we naturally leverage the complementary information of multiview data upon the same partition space. In order to boost the robustness of our approach, the marginalized denoising process is adopted to mimic the data corruptions and noises, which provides robust partition-level representations for each view by training a single-layer autoencoder. A low-rank and sparse decomposition is seamlessly incorporated into the denoising process to explicitly capture the consistency information and meanwhile compensate the distinctness between heterogeneous features. Spectral consensus graph partitioning is also involved by our model to make M 2 VEC as a unified optimization framework. Moreover, a multilayer M 2 VEC is eventually delivered in a stacked fashion to encapsulate nonlinearity into partition-level representations for handling complex data. Experimental results on eight real-world data sets show the efficacy of our approach compared with several state-of-the-art multiview and EC methods. We also showcase our method performs well with partial multiview data
Dual Information Enhanced Multi-view Attributed Graph Clustering
Multi-view attributed graph clustering is an important approach to partition
multi-view data based on the attribute feature and adjacent matrices from
different views. Some attempts have been made in utilizing Graph Neural Network
(GNN), which have achieved promising clustering performance. Despite this, few
of them pay attention to the inherent specific information embedded in multiple
views. Meanwhile, they are incapable of recovering the latent high-level
representation from the low-level ones, greatly limiting the downstream
clustering performance. To fill these gaps, a novel Dual Information enhanced
multi-view Attributed Graph Clustering (DIAGC) method is proposed in this
paper. Specifically, the proposed method introduces the Specific Information
Reconstruction (SIR) module to disentangle the explorations of the consensus
and specific information from multiple views, which enables GCN to capture the
more essential low-level representations. Besides, the Mutual Information
Maximization (MIM) module maximizes the agreement between the latent high-level
representation and low-level ones, and enables the high-level representation to
satisfy the desired clustering structure with the help of the Self-supervised
Clustering (SC) module. Extensive experiments on several real-world benchmarks
demonstrate the effectiveness of the proposed DIAGC method compared with the
state-of-the-art baselines.Comment: 11 pages, 4 figure
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant
attention owing to its high efficiency and the capability to capture
complementary structural information across multiple views. Intuitively, a
high-quality anchor graph plays an essential role in the success of AMVGC.
However, the existing AMVGC methods only consider single-structure information,
i.e., local or global structure, which provides insufficient information for
the learning task. To be specific, the over-scattered global structure leads to
learned anchors failing to depict the cluster partition well. In contrast, the
local structure with an improper similarity measure results in potentially
inaccurate anchor assignment, ultimately leading to sub-optimal clustering
performance. To tackle the issue, we propose a novel anchor-based multi-view
graph clustering framework termed Efficient Multi-View Graph Clustering with
Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified
framework with a theoretical guarantee is designed to capture local and global
information. Besides, EMVGC-LG jointly optimizes anchor construction and graph
learning to enhance the clustering quality. In addition, EMVGC-LG inherits the
linear complexity of existing AMVGC methods respecting the sample number, which
is time-economical and scales well with the data size. Extensive experiments
demonstrate the effectiveness and efficiency of our proposed method.Comment: arXiv admin note: text overlap with arXiv:2308.1654
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization
Multi-view clustering (MVC) optimally integrates complementary information
from different views to improve clustering performance. Although demonstrating
promising performance in various applications, most of existing approaches
directly fuse multiple pre-specified similarities to learn an optimal
similarity matrix for clustering, which could cause over-complicated
optimization and intensive computational cost. In this paper, we propose late
fusion MVC via alignment maximization to address these issues. To do so, we
first reveal the theoretical connection of existing k-means clustering and the
alignment between base partitions and the consensus one. Based on this
observation, we propose a simple but effective multi-view algorithm termed
LF-MVC-GAM. It optimally fuses multiple source information in partition level
from each individual view, and maximally aligns the consensus partition with
these weighted base ones. Such an alignment is beneficial to integrate
partition level information and significantly reduce the computational
complexity by sufficiently simplifying the optimization procedure. We then
design another variant, LF-MVC-LAM to further improve the clustering
performance by preserving the local intrinsic structure among multiple
partition spaces. After that, we develop two three-step iterative algorithms to
solve the resultant optimization problems with theoretically guaranteed
convergence. Further, we provide the generalization error bound analysis of the
proposed algorithms. Extensive experiments on eighteen multi-view benchmark
datasets demonstrate the effectiveness and efficiency of the proposed
LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The
codes of the proposed algorithms are publicly available at
https://github.com/wangsiwei2010/latefusionalignment
Fast Continual Multi-View Clustering with Incomplete Views
Multi-view clustering (MVC) has gained broad attention owing to its capacity
to exploit consistent and complementary information across views. This paper
focuses on a challenging issue in MVC called the incomplete continual data
problem (ICDP). In specific, most existing algorithms assume that views are
available in advance and overlook the scenarios where data observations of
views are accumulated over time. Due to privacy considerations or memory
limitations, previous views cannot be stored in these situations. Some works
are proposed to handle it, but all fail to address incomplete views. Such an
incomplete continual data problem (ICDP) in MVC is tough to solve since
incomplete information with continual data increases the difficulty of
extracting consistent and complementary knowledge among views. We propose Fast
Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it.
Specifically, it maintains a consensus coefficient matrix and updates knowledge
with the incoming incomplete view rather than storing and recomputing all the
data matrices. Considering that the views are incomplete, the newly collected
view might contain samples that have yet to appear; two indicator matrices and
a rotation matrix are developed to match matrices with different dimensions.
Besides, we design a three-step iterative algorithm to solve the resultant
problem in linear complexity with proven convergence. Comprehensive experiments
on various datasets show the superiority of FCMVC-IV
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