Advances in Data Science

Preface
Project Descriptions
Chapter Summary
Acknowledgment
Contents
Part I Image Processing
Two-stage Geometric Information Guided Image Reconstruction
1 Introduction
1.1 Background
2 Review of Shearlet Transform
3 Proposed Model and Algorithm
3.1 Stage I: TV-L1-L2 Model
3.2 Stage II: wTV-L1-L2 Model
4 Convergence Analysis
5 Numerical Examples
5.1 Example 1
5.2 Example 2
5.3 Example 3
6 Conclusion and Remarks
References
Image Edge Sharpening via Heaviside Substitution and Structure Recovery
1 Introduction
2 The Proposed Edge Sharpening Method
2.1 Heaviside Function
2.2 1D Heaviside Function Substitution
2.3 2D Image Extension
3 Structure Recovery
4 Results and Discussions
4.1 Application to Image Super-Resolution
4.1.1 Parameter Issues
4.1.2 Discussions on Different Initial High-Resolution Images
4.1.3 Results with Large Upscaling Factors
4.1.4 Limitations
4.2 Application to Image Deblurring
4.3 Application to Edge Sharpening
5 Conclusions
References
Two-Step Blind Deconvolution of UPC-A Barcode Images
1 Introduction
2 Our Approach
2.1 Kernel Estimation
2.2 Image Deblurring
3 Convergence Analysis
4 Experiment
4.1 Synthetic Data Experiment
4.2 Real Data Experiment
4.3 Empirical Verification
5 Conclusions
References
Part II Shape and Geometry
An Anisotropic Local Method for Boundary Detection in Images
1 Introduction
1.1 Related Work
2 Anisotropic Locally Adaptive Discriminant Analysis
2.1 Visualizing ALADA
2.2 Maximum Likelihood Estimation p-Value
3 Results
3.1 Berkeley Benchmark Images
3.2 Real Data
4 Conclusions
References
Towards Learning Geometric Shape Parts
1 Motivation
2 Background Fundamentals
2.1 Blum Medial Axis
2.1.1 Weighted Extended Distance Function
2.1.2 Defining a Clean Skeleton
2.1.3 Bézier Curve Approximation
2.2 Convolutional Neural Networks for Regression
3 A Canonical Parametric Medial Axis
3.1 Canonical Ordering of Linked Medial Branches
3.1.1 WEDF for Canonical Linked Medial Branches
3.2 Extracting a Stable Parametric Medial Axis
3.2.1 Clean Skeletons and a Minimal Skeletal Representation
3.2.2 Reducing Dimension Variability in the Medial Axis: Bézier Fit
4 Learning a Partial Parametric Medial Axis Using CNN
4.1 A Partial Representation of the Shape
4.2 Constructing the Neural Network
5 Results
5.1 General Shape: 1 Branch Model
5.2 Adding a Connected Branch: 2 Branches Model
5.3 Learning Shape Details: 5 Branch Model
6 Discussion and Future Work
References
Machine Learning in LiDAR 3D Point Clouds
1 Introduction
2 The Data
3 Feature Engineering: Nearest Neighbor Matrix
4 Machine Learning Frameworks
4.1 Dimension Reduction
5 Classification Experiments
6 Summary and Future Research Directions
References
Part III Machine Learning
Fitting Small Piece-Wise Linear Neural Network Models to Interpolate Data Sets
1 Introduction
2 Paper Overview
3 Related Work
4 An Example: Xor Is Not Interpolated by a One-LayerFunction
5 Two Layer One Weight Models 2L1W
5.1 Generic, Strictly Generic and Non-generic Weights
5.2 Definition of a Two Layer One Weight Model 2L1W
5.3 Sequential Variation
6 Two Additional Models
6.1 The Two Layer Sum Model: 2LS
6.2 The Three Layer Binary Model: BIN
7 Summary and Research Directions
Appendix: Results on Example 2D Data Sets
Appendix: Results on Example 2D Data Sets
Description of Sequential Variation Results
Description of Model Results
Description of Model Results
Model Results for the Xor Data Set
Model Results for the Generalized Xor Data Set
Model Results for the Generalized Xor Data Set
Model Graphs for the Synthetic Movie Ratings Data Set
Model Graphs for the Cluster Data Set
Result Figures
References
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
1 Introduction
2 Overview and Notations
2.1 NMF-Based Nonnegative Tensor Decompositions
2.1.1 NMF for Matrices
2.1.2 Direct NMF and Fixed NMF
2.2 CANDECOMP/PARAFAC (CP) Decomposition and NNCPD
2.2.1 Methodology of CP Decomposition and NNCPD
2.2.2 Existence and Uniqueness of Rank-r NNCPD
3 Comparison of NNCPD and NMF-Based Nonnegative Tensor Decompositions
3.1 Synthetic Dataset Numerical Experiments
3.1.1 Monotonic Dynamic Topic Modeling Dataset Experiment
3.1.2 Complex Dynamic Topic Modeling Dataset Experiment
3.2 The 20 Newsgroups Dataset Numerical Experiments
3.3 Noise Dataset Robustness Numerical Experiments
3.3.1 Construction of the Noise Dataset
3.3.2 Experiment Output on Noise Dataset
4 Conclusion
References
A Simple Recovery Framework for Signals with Time-Varying Sparse Support
1 Introduction
1.1 Related Work
1.2 Organization
2 Windowed Framework
2.1 Description of Framework
3 Example MMV Algorithms
3.1 MMV Sparse Randomized Kaczmarz with Prior Information
3.2 Weighted L2,1-Minimization
3.3 Weighted MMV Stochastic Gradient Matching Pursuit
4 Experiments
4.1 Experiments with Synthetic Data
4.2 Experiments with Real-World Data
4.3 Computational Cost
5 Conclusion
References
Part IV Data Analysis
Role Detection and Prediction in Dynamic Political Networks
1 Introduction
2 Related Work
3 Methodology
3.1 Role Discovery
3.2 Dynamic Role Prediction
4 Empirical Evaluation
4.1 Data Processing and Graph Creation
4.2 Feature Calculation
4.3 Role Results and Analysis
4.4 Prediction and Validation Results
5 Conclusion and Future Work
References
Classifying Sleep States Using Persistent Homology and Markov Chains: A Pilot Study
1 Introduction
2 Sleep State Analysis Using Persistent Homology
2.1 Background
2.1.1 Persistent Homology
2.1.2 Time Series Analysis
2.2 Results
3 Visualizing Sleep Patterns of Eight OSA Patients
4 Conclusion and Future Research
Appendix
References
A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data
1 Introduction
2 Pediatric Obstructive Sleep Apnea and Data
2.1 Survey Data
2.2 Craniofacial Data
2.3 Cleaning Data
3 Data Exploration
3.1 Correlation Networks
3.2 Mapper Algorithm
3.3 Singular Value Decomposition
4 Statistical Learning Methods
4.1 Non-Bayesian Supervised Learning
4.2 Bayesian Classifiers
4.3 Unsupervised Learning
4.3.1 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
4.3.2 Cut-Cluster-Classify (CCC)
4.3.3 Spectral Clustering
4.3.4 Continuous k-Nearest Neighbour Approach (CkNN)
4.3.5 Distance Metrics and Thresholding Density Using qk
5 Results
5.1 Results for Survey Data
5.2 Results for Craniofacial Data
5.2.1 Results: CF Distributions
5.2.2 Results: Classification with Craniofacial Data
5.3 Results for Combined Survey and Craniofacial Data
6 Conclusion and Future Research
Appendix
References
Nonparametric Estimation of Blood Alcohol Concentration from Transdermal Alcohol Measurements Using Alcohol Biosensor Devices
1 Introduction
1.1 Alcohol Biosensor Devices
2 Overview
3 Methods
3.1 Partial Differential Equation Model Simulation
3.1.1 Model Discretization
3.1.2 Simulation of Population Data
3.2 Nonparametric Maximum Likelihood Estimator
3.2.1 Nonparametric Estimation Schema
3.2.2 Reduction to Finite Support
3.3 Nonparametric Adaptive Grid Algorithm
3.3.1 Consistency and Convergence of the NPAG Algorithm
4 Results of the Synthetic Data Experiments
5 Conclusions
Appendix
References
Appendix
The Third WiSh Workshop Participants and Affiliations at the Time of the Workshop
The Second WiSDM Workshop Participants and Affiliations at the Time of the Workshop