15 research outputs found
Large-scale Multi-view Subspace Clustering in Linear Time
A plethora of multi-view subspace clustering (MVSC) methods have been
proposed over the past few years. Researchers manage to boost clustering
accuracy from different points of view. However, many state-of-the-art MVSC
algorithms, typically have a quadratic or even cubic complexity, are
inefficient and inherently difficult to apply at large scales. In the era of
big data, the computational issue becomes critical. To fill this gap, we
propose a large-scale MVSC (LMVSC) algorithm with linear order complexity.
Inspired by the idea of anchor graph, we first learn a smaller graph for each
view. Then, a novel approach is designed to integrate those graphs so that we
can implement spectral clustering on a smaller graph. Interestingly, it turns
out that our model also applies to single-view scenario. Extensive experiments
on various large-scale benchmark data sets validate the effectiveness and
efficiency of our approach with respect to state-of-the-art clustering methods.Comment: Accepted by AAAI 202
Scalable Multi-view Clustering via Explicit Kernel Features Maps
A growing awareness of multi-view learning as an important component in data
science and machine learning is a consequence of the increasing prevalence of
multiple views in real-world applications, especially in the context of
networks. In this paper we introduce a new scalability framework for multi-view
subspace clustering. An efficient optimization strategy is proposed, leveraging
kernel feature maps to reduce the computational burden while maintaining good
clustering performance. The scalability of the algorithm means that it can be
applied to large-scale datasets, including those with millions of data points,
using a standard machine, in a few minutes. We conduct extensive experiments on
real-world benchmark networks of various sizes in order to evaluate the
performance of our algorithm against state-of-the-art multi-view subspace
clustering methods and attributed-network multi-view approaches
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is
becoming a hot research spot, which aims to mine the potential relationships
between different views. Most existing DCMVC algorithms focus on exploring the
consistency information for the deep semantic features, while ignoring the
diverse information on shallow features. To fill this gap, we propose a novel
multi-view clustering network termed CodingNet to explore the diverse and
consistent information simultaneously in this paper. Specifically, instead of
utilizing the conventional auto-encoder, we design an asymmetric structure
network to extract shallow and deep features separately. Then, by aligning the
similarity matrix on the shallow feature to the zero matrix, we ensure the
diversity for the shallow features, thus offering a better description of
multi-view data. Moreover, we propose a dual contrastive mechanism that
maintains consistency for deep features at both view-feature and pseudo-label
levels. Our framework's efficacy is validated through extensive experiments on
six widely used benchmark datasets, outperforming most state-of-the-art
multi-view clustering algorithms
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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)
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
High-order Multi-view Clustering for Generic Data
Graph-based multi-view clustering has achieved better performance than most
non-graph approaches. However, in many real-world scenarios, the graph
structure of data is not given or the quality of initial graph is poor.
Additionally, existing methods largely neglect the high-order neighborhood
information that characterizes complex intrinsic interactions. To tackle these
problems, we introduce an approach called high-order multi-view clustering
(HMvC) to explore the topology structure information of generic data. Firstly,
graph filtering is applied to encode structure information, which unifies the
processing of attributed graph data and non-graph data in a single framework.
Secondly, up to infinity-order intrinsic relationships are exploited to enrich
the learned graph. Thirdly, to explore the consistent and complementary
information of various views, an adaptive graph fusion mechanism is proposed to
achieve a consensus graph. Comprehensive experimental results on both non-graph
and attributed graph data show the superior performance of our method with
respect to various state-of-the-art techniques, including some deep learning
methods
One-step Multi-view Clustering with Diverse Representation
Multi-view clustering has attracted broad attention due to its capacity to
utilize consistent and complementary information among views. Although
tremendous progress has been made recently, most existing methods undergo high
complexity, preventing them from being applied to large-scale tasks. Multi-view
clustering via matrix factorization is a representative to address this issue.
However, most of them map the data matrices into a fixed dimension, which
limits the expressiveness of the model. Moreover, a range of methods suffer
from a two-step process, i.e., multimodal learning and the subsequent
-means, inevitably causing a sub-optimal clustering result. In light of
this, we propose a one-step multi-view clustering with diverse representation
method, which incorporates multi-view learning and -means into a unified
framework. Specifically, we first project original data matrices into various
latent spaces to attain comprehensive information and auto-weight them in a
self-supervised manner. Then we directly use the information matrices under
diverse dimensions to obtain consensus discrete clustering labels. The unified
work of representation learning and clustering boosts the quality of the final
results. Furthermore, we develop an efficient optimization algorithm to solve
the resultant problem with proven convergence. Comprehensive experiments on
various datasets demonstrate the promising clustering performance of our
proposed method