4 research outputs found
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Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.This research was funded by National Natural Science Foundation of China under Grant
61903091; Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010801)
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
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
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