Differentiable Information Bottleneck for Deterministic Multi-view Clustering

Core Concepts

Proposing a novel Differentiable Information Bottleneck method for deterministic multi-view clustering without variational approximation.

Abstract

This article introduces the Differentiable Information Bottleneck (DIB) method for deterministic multi-view clustering. It addresses the limitations of existing methods by directly fitting mutual information without variational approximation. The DIB approach includes deterministic compression and triplet consistency discovery mechanisms, leading to superior performance compared to state-of-the-art baselines on various datasets. Introduction Discusses traditional MVC techniques and the shift towards deep learning models. Introduces the concept of the information bottleneck principle in multi-view clustering. Related Work and Preliminaries Explains the information bottleneck concept and its application in deep multi-view clustering. Differentiable Information Bottleneck Proposes a new method for deterministic compression and triplet consistency discovery. Defines problem statements and objectives for multi-view clustering. Experiments Evaluates DIB on six datasets, comparing it with traditional, deep, and IB-based MVC baselines. Ablation Study Analyzes the impact of different components on clustering performance. Parameter Sensitivity Analysis Investigates the effect of trade-off parameters γ and β on clustering performance. Convergence Analysis Demonstrates convergence behavior of the DIB algorithm over iterations. MI Measurement Evaluation Compares mutual information with and without variational approximation.

Stats

Variational approximation offers a natural solution to estimate lower bound mutual information in high-dimensional spaces. The proposed MI measurement directly fits mutual information between high-dimensional spaces using normalized kernel Gram matrix.

Quotes

"The proposed DIB method provides a deterministic and analytical MVC solution." "DIB outperforms traditional MVC, deep MVC, and IB-based deep MVC baselines."

Key Insights Distilled From

Differentiable Information Bottleneck for Deterministic Multi-view Clustering

by Xiaoqiang Ya... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15681.pdf

Deeper Inquiries

How can the DIB method be extended to handle noisy or incomplete data?

The DIB method can be extended to handle noisy or incomplete data by incorporating robust feature learning techniques. One approach could involve integrating denoising autoencoders into the model architecture to learn more resilient representations from noisy input data. Additionally, techniques like dropout regularization or batch normalization can help mitigate the effects of noise in the data. For handling incomplete data, imputation methods such as mean substitution, regression imputation, or matrix completion algorithms can be integrated into the DIB framework to fill in missing values and ensure a more comprehensive representation of the multi-view data.

What are potential applications of the DIB approach beyond multi-view clustering?

Beyond multi-view clustering, the DIB approach has promising applications in various domains that require feature extraction and representation learning. Some potential applications include: Anomaly Detection: The deterministic compression aspect of DIB could be utilized for anomaly detection tasks by capturing inconsistencies in high-dimensional features across different views. Image Recognition: In image recognition tasks, DIB could aid in extracting discriminative features from multiple perspectives or modalities within images for improved classification accuracy. Natural Language Processing: For NLP tasks involving multiple sources of textual information, DIB could assist in learning compact representations that capture semantic similarities and differences across texts. Healthcare Informatics: In healthcare informatics, DIB could be applied to integrate diverse patient data sources (e.g., medical images, lab reports) for better patient profiling and diagnosis.

How does incorporating triplet consistency enhance feature representation learning in DIB?

Incorporating triplet consistency enhances feature representation learning in DIB by enforcing relationships between high-level features derived from different views while considering both positive and negative instances simultaneously: Feature Consistency: By comparing each feature with all other features except itself (forming positive and negative pairs), triplet consistency ensures that similar instances are closer together while dissimilar ones are pushed apart. Cluster Consistency: Triplet consistency aligns cluster labels with high-level semantics across views through contrastive learning approaches, ensuring consistent cluster assignments based on shared characteristics among samples. Joint Consistency: Joint consistency further refines this process by maximizing mutual information between high-level features and cluster assignments across multiple views, facilitating a holistic understanding of common patterns present in different modalities. By leveraging triplet consistency mechanisms within the framework of deterministically compressed representations provided by DIB, it enables more robust and meaningful feature extraction that captures essential correlations among diverse sources of information for improved clustering performance and generalization capabilities across various datasets and domains.