One-Step Late Fusion Multi-view Clustering with Compressed Subspace for Efficient and Effective Clustering

Belangrijkste concepten

The proposed One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS) method directly obtains discrete cluster labels by integrating clustering structure optimization and label learning into a unified framework, achieving high efficiency and effectiveness.

Samenvatting

The paper proposes a novel One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS) method to address the limitations of existing multi-view clustering approaches. Key highlights:

The method integrates clustering structure optimization and label learning into a unified framework, allowing it to directly obtain discrete cluster labels in one step, without the need for additional post-processing steps.
It adopts a kernel subspace clustering approach for self-reconstruction of the consensus kernel partition, and uses a compressed subspace to further increase computational efficiency.
The optimization is formulated as a six-step iterative process with proven convergence, leading to high efficiency with linear time and space complexity.
Extensive experiments on benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, outperforming various state-of-the-art multi-view clustering algorithms.

Statistieken

The paper reports the following key metrics:

Accuracy (ACC) on Citeseer: 56.6%
Normalized Mutual Information (NMI) on Cora: 37.8%
Purity on Reuters: 61.9%

Citaten

"Our algorithm is able to obtain clustering labels in one step, by negotiating label learning and cluster structure optimization through a unified framework."
"The method is highly efficient with both O(n) time and space expenditure, which allows our algorithm to be used directly on large-scale multi-view datasets."

Belangrijkste Inzichten Gedestilleerd Uit

One-Step Late Fusion Multi-view Clustering with Compressed Subspace

by Qiyuan Ou,Pe... om arxiv.org 04-10-2024

https://arxiv.org/pdf/2401.01558.pdf

Diepere vragen

How can the proposed OS-LFMVC-CS method be extended to handle dynamic or streaming multi-view data

To extend the proposed OS-LFMVC-CS method to handle dynamic or streaming multi-view data, several modifications and enhancements can be implemented. One approach is to incorporate incremental learning techniques that can adapt to new data instances as they arrive. This can involve updating the consensus subspace and partition matrices in real-time based on the incoming data streams. Additionally, the algorithm can be designed to dynamically adjust the compression matrix and fusion weights to accommodate changes in the data distribution over time. By integrating online learning strategies and adaptive parameter tuning, the OS-LFMVC-CS method can effectively handle dynamic multi-view data streams while maintaining clustering performance.

What are the potential limitations of the compressed subspace approach, and how can it be further improved to maintain clustering performance on diverse datasets

The compressed subspace approach in the OS-LFMVC-CS method may have limitations in scenarios where the data distribution is highly non-linear or when the clusters exhibit complex structures. To address these limitations and improve the method's performance on diverse datasets, several enhancements can be considered. One approach is to incorporate non-linear dimensionality reduction techniques, such as kernel methods or deep learning models, to capture intricate relationships in the data. Additionally, exploring adaptive compression strategies that can adjust the subspace dimensionality based on the data characteristics can enhance the method's flexibility. Moreover, integrating robust optimization techniques to handle outliers and noise in the data can improve the clustering performance on challenging datasets.

What other applications beyond multi-view clustering could benefit from the efficient one-step optimization framework developed in this work

The efficient one-step optimization framework developed in this work can benefit various applications beyond multi-view clustering. One potential application is in anomaly detection, where the framework can be utilized to optimize the detection of anomalous patterns in complex data streams. By leveraging the one-step optimization approach, anomaly detection models can efficiently learn from multi-modal data sources and adapt to changing patterns in real-time. Furthermore, the framework can be applied to semi-supervised learning tasks, such as label propagation and data integration, where the goal is to leverage both labeled and unlabeled data for improved predictive modeling. By extending the one-step optimization framework to these applications, it can enhance the efficiency and effectiveness of various machine learning tasks.