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Updated: May 29, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
A step towards unification of syntactic and statistical pattern recognition.
1School of Electrical Engineering, Purdue University. West Lafayette. IN 47907.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study explores pattern recognition using single versus multiple entity representations. A novel syntactic-semantic approach using attributed grammars is proposed for improved pattern analysis.
Area of Science:
- Computer Science
- Artificial Intelligence
- Pattern Recognition
Background:
- Traditional pattern recognition often relies on single-entity or multiple-entity representations.
- Existing methods may not fully capture complex relationships within data patterns.
- A gap exists in unifying syntactic and statistical pattern recognition techniques.
Purpose of the Study:
- To investigate the effectiveness of single-entity versus multiple-entity representations in pattern recognition.
- To propose a combined syntactic-semantic approach for enhanced pattern representation.
- To demonstrate the syntax-semantics tradeoff in representing complex patterns.
Main Methods:
- Utilizing attributed grammars to model syntactic and semantic features of patterns.
- Comparing single-entity and multiple-entity representation strategies.
- Analyzing the impact of syntax-semantics tradeoff on recognition accuracy.
Main Results:
- The combined syntactic-semantic approach shows promise for pattern recognition.
- Demonstrated a quantifiable syntax-semantics tradeoff in pattern representation.
- The proposed method offers a flexible framework for handling diverse patterns.
Conclusions:
- A combined syntactic-semantic approach based on attributed grammars is a viable method for pattern recognition.
- This work serves as a foundational step towards unifying syntactic and statistical pattern recognition.
- Further research can explore the application of this approach to various real-world pattern recognition problems.
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