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S2MVTC: Efficient Multi-View Tensor Clustering Method


Core Concepts
Efficiently learn inter-and intra-view consistency among embedding features in large-scale multi-view datasets.
Abstract
The S2MVTC method proposes a simple yet efficient approach for multi-view tensor clustering. It focuses on learning correlations of embedding features within and across views by utilizing a tensor low-frequency approximation (TLFA) operator. This operator incorporates graph similarity into embedding feature learning, ensuring smooth representation of samples within different views. Consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results show that S2MVTC outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time, especially with massive data sizes.
Stats
Experimental results on six large-scale multi-view datasets demonstrate that S2MVTC significantly outperforms state-of-the-art algorithms. The code of S2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.
Quotes
"No need to explore the global correlations between anchor graphs or projection matrices." "Why not directly explore the correlations between embedding features from different views?" "Two questions naturally arise: Why not directly explore the correlations between embedding features from different views?"

Key Insights Distilled From

by Zhen Long,Qi... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09107.pdf
S^2MVTC

Deeper Inquiries

How can the incorporation of inter-and intra-view consistency improve clustering performance

Incorporating inter-and intra-view consistency can significantly improve clustering performance by enhancing the overall quality and robustness of the clustering results. When exploring correlations between embedding features from different views, we are essentially leveraging complementary information present in each view to create a more comprehensive representation of the data. This approach helps in capturing diverse perspectives and nuances that may not be apparent when considering individual views separately. By learning both inter-view relationships (consistency across different views) and intra-view relationships (consistency within the same view), we can achieve a more holistic understanding of the dataset, leading to improved cluster formation based on shared patterns and similarities across multiple dimensions.

What are the implications of removing the exploration of graph similarity among intra-view features

Removing the exploration of graph similarity among intra-view features can have detrimental effects on clustering performance. Graph similarity plays a crucial role in identifying patterns and relationships within each view, which is essential for creating meaningful clusters. Without this exploration, important connections between samples within the same view may go unnoticed or underutilized during the clustering process. As a result, the algorithm may struggle to capture fine-grained distinctions or subtle variations present in individual views, leading to less accurate cluster assignments and potentially lower overall performance.

How does a nonlinear anchor graph contribute to capturing relationships between samples in large datasets

A nonlinear anchor graph contributes significantly to capturing relationships between samples in large datasets by allowing for more complex mappings that better represent intricate data structures. In large datasets with high-dimensional feature spaces, linear transformations may not adequately capture the underlying non-linear relationships between samples. Nonlinear anchor graphs provide a flexible framework that can model these complex interactions effectively, enabling better alignment of anchor points with actual data points even in high-dimensional spaces. By incorporating nonlinearities into anchor graphs, we enhance our ability to capture intricate patterns and dependencies present in multi-dimensional data accurately. This flexibility allows us to adapt better to varying degrees of complexity within datasets, especially as they grow larger and exhibit more intricate structures that cannot be captured through linear mappings alone.
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