The Dissimilarity Representation for Pattern Recognition Foundations and Applications 1st edition by Robert P W Duin, Elzbieta Pekalska ISBN 9812565302  978-9812565303

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ISBN 10: 9812565302
ISBN 13: 978-9812565303
Author: Robert P W Duin, Elzbieta Pekalska 

This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This ‘dissimilarity representation’ bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition.Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and integrate by machine learning procedures. However, if the relations are captured by sets of dissimilarities, general data analysis procedures may be applied for analysis.With their detailed description of an unprecedented approach absent from traditional textbooks, the authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition systems.

The Dissimilarity Representation for Pattern Recognition : Foundations and Applications 1st Table of contents:

Preface

• Introduction to the Concept of Dissimilarity Representation
• Why Dissimilarity Representation in Pattern Recognition?
• Structure and Scope of the Book
• Target Audience and Prerequisites

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Part I: Foundations of Dissimilarity Representation

1. Introduction to Pattern Recognition

   • Overview of Pattern Recognition Techniques
   • Challenges in Traditional Pattern Recognition Approaches
   • Key Concepts in Dissimilarity-Based Approaches
   • Dissimilarity vs. Similarity-Based Representations

2. Mathematical Foundations of Dissimilarity Representation

   • Defining Dissimilarity: Distance Metrics and Measures
   • Properties of Dissimilarity: Symmetry, Non-Negativity, and Triangular Inequality
   • Common Dissimilarity Measures: Euclidean, Mahalanobis, Cosine, etc.
   • Dissimilarity Matrices and their Properties
   • Dissimilarity Representations in High-Dimensional Spaces

3. Theoretical Framework for Dissimilarity Representation

   • Representing Objects as Dissimilarity Matrices
   • The Role of Dissimilarity Representation in Non-Metric Spaces
   • Transformation and Embedding of Dissimilarity Data
   • Metric and Non-Metric Approaches in Dissimilarity
   • Mathematical Properties of Dissimilarity Representation in Classification

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Part II: Dissimilarity-Based Learning Methods

4. Dissimilarity-Based Classification

   • Dissimilarity-Based Classifiers: k-Nearest Neighbors (k-NN) and Beyond
   • Non-Linear Classification Using Dissimilarity Matrices
   • Distance-Based Metrics for Class Separation
   • Algorithmic Approaches for Dissimilarity-Based Classifiers
   • Evaluation and Validation of Dissimilarity-Based Classifiers

5. Clustering and Dissimilarity Representation

   • Clustering in Dissimilarity Spaces
   • Hierarchical Clustering Using Dissimilarity Measures
   • k-Means and k-Medoids Clustering with Dissimilarity Matrices
   • Spectral Clustering and its Relationship to Dissimilarity Representation
   • Clustering Algorithms Based on Pairwise Dissimilarities

6. Dimensionality Reduction for Dissimilarity Representation

   • Reducing Dissimilarity Matrices to Lower Dimensions
   • Multidimensional Scaling (MDS) and Isomap
   • t-SNE and UMAP for Visualization of Dissimilarity Data
   • Kernel Methods in Dissimilarity Representation
   • Latent Variable Models and Factor Analysis for Dissimilarity Matrices

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Part III: Advanced Topics in Dissimilarity Representation

7. Dissimilarity Representation in Deep Learning

   • Deep Learning Approaches for Dissimilarity-Based Data
   • Neural Networks with Dissimilarity Measures
   • Metric Learning and Its Role in Dissimilarity-Based Models
   • Siamese Networks and Triplet Loss for Learning Dissimilarity Metrics
   • Hybrid Models Combining Deep Learning with Dissimilarity Representation

8. Dissimilarity Representation in Structured Data

   • Using Dissimilarity for Graphs and Networks
   • Dissimilarity Representation for Sequential and Time-Series Data
   • Applications to Natural Language Processing: Word Embeddings and Dissimilarity
   • Dissimilarity Representation for Image and Video Data
   • Modeling Complex Data Types with Dissimilarity Metrics

9. Multiview and Multimodal Dissimilarity Representation

   • Combining Dissimilarity Measures from Different Sources
   • Fusion Techniques for Dissimilarity Representation Across Modalities
   • Cross-Domain Dissimilarity Learning
   • Multiview Learning with Dissimilarity Matrices
   • Applications to Multimodal Data in Healthcare and Multimedia

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Part IV: Applications of Dissimilarity Representation

10. Applications in Computer Vision

    • Object and Image Recognition Using Dissimilarity Representation
    • Dissimilarity-Based Techniques for Face Recognition and Image Classification
    • Visual Pattern Recognition with Pairwise Dissimilarity Measures
    • Dissimilarity Representation in Image Retrieval Systems
    • Deep Dissimilarity Learning for Object Detection

11. Applications in Bioinformatics

    • Sequence Alignment and Dissimilarity Representation
    • Protein and DNA Sequence Classification Using Dissimilarity Matrices
    • Clustering and Phylogenetic Tree Construction with Dissimilarity
    • Dissimilarity-Based Models for Gene Expression Data Analysis
    • Biomedical Image Analysis and Dissimilarity-Based Classification

12. Applications in Text and Document Mining

    • Text Classification and Clustering with Dissimilarity Measures
    • Topic Modeling and Semantic Dissimilarity in Text Data
    • Natural Language Processing with Dissimilarity-Based Models
    • Document Clustering and Retrieval Using Pairwise Dissimilarity
    • Dissimilarity-Based Approaches in Sentiment Analysis

13. Applications in Speech and Audio Processing

    • Speech Recognition Using Dissimilarity Representation
    • Audio Classification and Clustering with Dissimilarity Measures
    • Audio Feature Extraction Using Dissimilarity Metrics
    • Dissimilarity-Based Speech Emotion Recognition
    • Applications in Music Genre Classification and Audio Retrieval

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Part V: Future Directions and Challenges

14. Challenges in Dissimilarity-Based Pattern Recognition

    • Scalability and Efficiency of Dissimilarity-Based Methods
    • Handling Noisy, Incomplete, and High-Dimensional Dissimilarity Data
    • Generalization and Overfitting in Dissimilarity-Based Models
    • Interpretability and Explainability of Dissimilarity Models
    • Robustness and Outlier Detection in Dissimilarity Representations

15. Emerging Trends and Research Directions

    • Advancements in Metric Learning and Dissimilarity Measures
    • Dissimilarity Representation for Big Data and Streaming Data
    • Hybrid Approaches: Dissimilarity Representation and Deep Learning
    • Cross-Domain Applications and Transfer Learning with Dissimilarity
    • The Role of Dissimilarity Representation in AI and Machine Learning

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Conclusion

• Summary of Key Concepts and Techniques in Dissimilarity Representation
• Final Thoughts on the Impact of Dissimilarity-Based Approaches in Pattern Recognition
• Future Prospects for Dissimilarity Representation in Emerging Technologies

References

• Key Academic References, Papers, and Further Reading

Index

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