Feature–pivot methods

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Table of contents

1 Introduction 
1.1 Dynamic Social Networks
1.1.1 The Twitter Social Network
1.2 Research and Technical Challenges
1.3 Problem Statement and Objectives
1.4 Scope and Plan of the Thesis
2 Background and Related Work 
2.1 Introduction
2.2 Document–pivot methods
2.3 Feature–pivot methods
2.4 Related Work
2.4.1 Problem Definition
2.4.2 Data Preprocessing
2.4.3 Latent Dirichlet Allocation
2.4.4 Document–Pivot Topic Detection
2.4.5 Graph–Based Feature–Pivot Topic Detection
2.4.6 Frequent Pattern Mining
2.4.7 Soft Frequent Pattern Mining
2.4.8 BNgram
2.5 Chapter Summary
3 Joint Sequence Complexity: Introduction and Theory 
3.1 Introduction
3.2 Sequence Complexity
3.3 Joint Complexity
3.4 Main Results
3.4.1 Models and Notations
3.4.2 Summary of Main Results
3.5 Proof of Main Results
3.5.1 An important asymptotic equivalence
3.5.2 Functional Equations
3.5.3 Double DePoissonization
3.5.4 Same Markov sources
3.5.5 Different Markov Sources
3.6 Expending Asymptotics and Periodic Terms
3.7 Numerical Experiments in Twitter
3.8 Suffix Trees
3.8.1 Examples of Suffix Trees
3.9 Snow Data Challenge
3.9.1 Topic Detection Method
3.9.2 Headlines
3.9.3 Keywords Extraction
3.9.4 Media URLs
3.9.5 Evaluation of Topic Detection
3.10 Tweet Classification
3.10.1 Tweet augmentation
3.10.2 Training Phase
3.10.3 Run Phase
3.10.4 Experimental Results on Tweet Classification
3.11 Chapter Summary
4 Text Classification via Compressive Sensing 
4.1 Introduction
4.2 Compressive Sensing Theory
4.3 Compressive Sensing Classification
4.3.1 Training Phase
4.3.2 Run Phase
4.4 Tracking via Kalman Filter
4.5 Experimental Results
4.5.1 Classification Performance based on Ground Truth
4.6 Chapter Summary
5 Extension of Joint Complexity and Compressive Sensing 
5.1 Introduction
5.2 Indoor Path-Tracking Using Compressive RSS Measurements .
5.2.1 Prior Work on RSS-based Path Tracking
5.2.2 CS-based Location Estimation
5.2.3 CS-Kalman Filter
5.2.4 Experimental Results
5.3 Encryption System based on Compressive Sensing Measurements .
5.3.1 SecLoc System Description
5.3.2 Possible Attacks from Malicious Users
5.3.3 Experimental Results
5.4 Stealth Encryption based on Eulerian Circuits
5.4.1 Background
5.4.2 Motivation and Algorithm Description
5.4.3 Performance in Markov Models
5.4.4 Experimental Results
5.5 Chapter Summary
6 Conclusions and Perspectives 
Bibliography 
A Suffix Trees 
A.1 Suffix Tree Construction
A.2 Suffix Trees Superposition