Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data.

Area of Science:

• Data Science
• Machine Learning
• Computer Science

Background:

• Tree structures are fundamental for organizing hierarchical data.
• Transforming complex tree data into machine-readable formats is crucial for analysis.
• Existing methods may not fully capture the global and local information within tree structures.

Purpose of the Study:

• To present an efficient learning framework for converting tree-structured data into vectorial representations.
• To develop a method that captures both global discriminative information and local dependencies within trees.
• To enable effective data organization and retrieval using learned vector embeddings.

Main Methods:

• A clustering technique is employed to group child nodes at the same level, forming initial vector components.
• A locality-sensitive reconstruction method models parent-child relationships, generating complementary coefficients.
• These coefficients are used to locally weight vector components, creating hierarchical representations recursively.

Main Results:

• The proposed framework effectively transforms tree-structured data into meaningful vectorial representations.
• Experimental results in book author recommendations and image retrieval demonstrate the method's efficacy.
• The learned vectors capture essential information for downstream tasks.

Conclusions:

• The developed framework provides an effective approach for vectorizing tree-structured data.
• The method successfully integrates global and local information for enhanced data representation.
• This technique offers a powerful tool for applications involving hierarchical data organization and analysis.