Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

Area of Science:

• Data Science
• Machine Learning
• Computer Science

Background:

• Stream View Clustering (SVC) methods struggle with unpredictable view arrival sequences and missing data.
• Existing SVC approaches exhibit performance bias due to early views dominating training and incomplete data.
• The Sequential Dependency Problem (SDP) and Partial Sample-missing Problem (PSP) hinder robust knowledge acquisition in SVC.

Purpose of the Study:

• To propose Adaptive hardness-Driven dictionary distillAtion for incomPlete streaming view clusTering (ADAPT).
• To mitigate the adverse effects of SDP and PSP in stream view clustering.
• To enhance the robustness and accuracy of clustering for continuously evolving data.

Main Methods:

• Utilized dictionary learning on the first complete view to establish an initial teacher knowledge base.
• Employed teacher prompting imputation to address the Partial Sample-missing Problem (PSP).
• Implemented adaptive hardness-driven dictionary distillation to dynamically adjust strategies based on view quality, mitigating the Sequential Dependency Problem (SDP).

Main Results:

• ADAPT effectively addresses both the Sequential Dependency Problem (SDP) and Partial Sample-missing Problem (PSP).
• The proposed adaptive hardness-driven dictionary distillation enables robust knowledge learning across varying view sequences.
• Cluster guidance learning enhanced clustering structure compactness, and teacher knowledge summarization updated the knowledge base.

Conclusions:

• ADAPT significantly outperforms existing state-of-the-art methods in stream view clustering.
• The method demonstrates superior performance in handling incomplete and sequentially dependent streaming view data.
• ADAPT offers a robust solution for real-world applications involving continuous, evolving data streams.