Fast Continual Multi-View Clustering with Incomplete Views
- URL: http://arxiv.org/abs/2306.02389v1
- Date: Sun, 4 Jun 2023 15:48:09 GMT
- Title: Fast Continual Multi-View Clustering with Incomplete Views
- Authors: Xinhang Wan, Bin Xiao, Xinwang Liu, Jiyuan Liu, Weixuan Liang, En Zhu
- Abstract summary: This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP)
Most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time.
We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it.
- Score: 44.94941453023393
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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