
Machine Learning and Knowledge Discovery in Databases, Part II
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Content
- Title
- Preface
- Organization
- Table of Contents
- Regular Papers
- Common Substructure Learning of Multiple Graphical Gaussian Models
- Introduction
- Structure Learning of Graphical Gaussian Model
- Graphical Gaussian Model
- Sparse Estimation of GGM
- Learning Structural Changes
- Multi-task Approach for Learning a Set of GGMs
- Common Substructure Learning
- Algorithm
- Block Coordinate Descent
- Subproblem
- Continuous Quadratic Knapsack Problem
- Hyper-Parameters ? and ?
- Simulation
- Synthetic Experiment
- Analysis of City-Cycle Fuel Consumption Data
- Application to Anomaly Detection
- Conclusion
- References
- Mining Research Topic-Related Influence between Academia and Industry
- Introduction
- Related Work
- Models
- Simple Additive Model
- Weighted Additive Model
- Clustering-Based Additive Model
- Experimental Results
- Experiments Settings
- Influence of Academia Researchers to Company
- Influence of Universities to Company
- Simulated Data
- Conclusion
- References
- Typology of Mixed-Membership Models:Towards a Design Method
- Introduction
- Networks of Mixed Membership
- Numerical Properties
- Model Structure
- Model Decomposition
- Typology of Sub-structures
- Towards a Model Design Method
- Designing a Design Method
- Example: Expert-Tag-Topic Model
- Empirical Analysis
- Conclusions and Future Work
- References
- ShiftTree: An Interpretable Model-Based Approach for Time Series Classification
- Introduction
- Related Works
- Classification of Time Series
- Problem Definition
- Notation
- Concept
- The ShiftTree Algorithm
- The Structure of a ShiftTree Node
- Classification Process
- Training Process
- About Interpretability
- Forest Methods for ShiftTree
- Boosting
- XV Method
- Numerical Results
- Datasets and Testing Environment
- Results of the Basic ShiftTree
- Results of the Forest Methods
- Results of the Blind Tests
- Conclusion
- References
- Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining
- Introduction
- Previous Work
- Age-related Macular Degeneration
- AMD Classifier Generation
- Image Decomposition
- Weighted Frequent Sub-tree (wFST) Mining
- Feature Selection
- Classification Technique
- Evaluation
- Performances Using Different Levels of Decomposition
- Performances of AMD Classification According to the Size of the Identified Feature Space
- Performance Comparison of AMD Classification Using Various Classification Techniques
- Conclusions
- References
- Online Structure Learning for Markov Logic Networks
- Introduction
- Background
- Terminology and Notation
- MLNs
- Natural Language Field Segmentation
- Online Max-Margin Structure and Parameter Learning
- Online Max-Margin Structure Learning with Mode-Guided Relational Pathfinding
- Online Max-Margin l1-Regularized Weight Learning
- Experimental Evaluation
- Data
- Input MLNs
- Methodology
- Results and Discussion
- Related Work
- Future Work
- Conclusions
- References
- Fourier-Information Duality in the Identity Management Problem
- Introduction
- Probabilistic Identity Management
- Inference Operations
- Two Dueling Representations
- Fourier Domain Representation
- Information Form Representation
- Comparing the Two Representations
- Discussion
- Representation Conversion
- From Information Coefficients to Fourier Coefficients
- From Fourier Coefficients to Information Coefficients
- Computation of the Matrix Permanent
- A Hybrid Approach for Identity Management
- An Adaptive Approach for Identity Management
- Experiments
- Conclusion
- References
- Eigenvector Sensitive Feature Selection for Spectral Clustering
- Introduction
- Feature Selection Based on Perturbation Analysis
- Problem Definition
- $delta$q$_t,r$ with Respect to L
- $delta$q$_rw,t,r$ with Respect to L$_rw$
- $delta$q$_sym,t,r$ with Respect to L$_sym$
- Eigenvector Sensitive Feature Selection
- Eigenvector Sensitive Feature Selection for Spectral Clustering
- Related Work
- Empirical Analysis
- Dataset Decription
- Evaluation Criterion
- Experiment Setup
- Experiment Results
- Conclusion
- References
- Restricted Deep Belief Networks for Multi-view Learning
- Introduction
- Related Work
- Exponential Family Harmonium
- Multi-Wing Harmonium
- Restricted DBNs
- Multi-view Harmonium
- Restricted DBN
- Inferring One View from the Other
- Numerical Experiments
- Synthetic Example
- Object Conversion on NORB-Small
- Image Annotation on ESL Photo Dataset
- Conclusions
- References
- Motion Segmentation by Model-Based Clustering of Incomplete Trajectories
- Introduction
- Extracting Trajectories
- Clustering Trajectories of Variable Length
- Initialization Strategy
- Experimental Results
- Experiments with Simulated Data Sets
- Experiments with Real Data Sets
- Conclusion
- References
- PTMSearch: A Greedy Tree Traversal Algorithm for Finding Protein Post-Translational Modifications in Tandem Mass Spectra
- Introduction
- MS/MS Spectra and PTMs
- Related Work
- Method: The PTMSearch Algorithm
- Speedup Techniques
- Significance Calculation of a Hit
- Experimental Results
- Results on a Toy Datasets
- Calculations on Real Data
- Discussion, Future Plans
- References
- Efficient Mining of Top Correlated Patterns Based on Null-Invariant Measures
- Introduction
- Preliminaries
- New Properties of Null-Invariant Measures
- Level-Based Properties
- Properties Based on a Single Item
- NICoMiner Algorithm
- Threshold-Based Correlation Mining
- Top-k Correlation Mining
- Experiments
- Synthetic Datasets
- Real Datasets
- Related Work
- Conclusions and Future Work
- References
- Smooth Receiver Operating Characteristics (smROC) Curves
- Introduction
- Motivation and Related Work
- Constructing a Smooth ROC Curve
- Experiments
- Performance Similarities
- Detecting Differences
- Conclusions
- References
- A Boosting Approach to Multiview Classification with Cooperation
- Introduction
- The Mumbo Algorithm
- Principles and Assumptions
- Framework and Notations
- The Core of Mumbo
- Properties of Mumbo
- Bounding the Training Error on Each View
- Bounding the Whole Empirical Error
- Results in Generalization
- Experiments on Mumbo
- Protocols
- Results
- Discussion
- Related Works and Discussion
- Related Works
- Discussion and Improvements
- Conclusion and Future Works
- References
- ARTEMIS: Assessing the Similarity of Event-Interval Sequences
- Introduction
- Event-Interval Sequences
- Distance Measures
- The Vector-Based DTW Distance
- Artemis: A Bipartite-Based Matching Distance
- Lower Bounding Artemis
- Experiments
- Experimental Setup
- Results
- Lessons Learned
- Related Work
- Summary and Conclusions
- References
- Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms
- Introduction
- Related Work
- Theorems and Correspondences
- Arithmetic Examples
- Analysis of Convergence
- Proposed Algorithm: FaBP
- Experiments
- Q1: Accuracy
- Q2: Convergence
- Q3: Sensitivity to Parameters
- Q4: Scalability
- Conclusions
- References
- Online Clustering of High-Dimensional Trajectories under Concept Drift
- Introduction
- Related Work
- Method TRACER
- Gaussian Mixture Model
- Expectation-Maximisation Algorithm
- Kalman Filter
- Kalman Filter Initialisation
- Update and Clustering
- Experiments
- Data Sets
- Methods
- Results
- Conclusions
- References
- Gaussian Logic for Predictive Classification
- Introduction
- A Probabilistic Framework
- Parameter Estimation
- Structure Search
- A Straightforward Predictive Classification Method
- Feature Construction for Predictive Classification
- Conclusions and Future Work
- References
- Toward a Fair Review-Management System
- Introduction
- Contribution
- Roadmap
- Related Work
- Notation
- Spotlight Shuffling
- Attribute Coverage
- Review Quality
- Fair Spotlight Share
- Compactness
- Reviewer Motivation and Utilization
- Experiments
- Datasets
- Qualitative Evidence
- Evaluation of ImportanceSampling on the Spotlight-Shuffling Task
- The Effect of the Seed of Minimal Covers on ImportanceSampling
- Compactness Evaluation
- Evaluating the Attribute-Recommendation System
- Conclusion
- References
- Focused Multi-task Learning Using Gaussian Processes
- Introduction
- Symmetric and Asymmetric Multi-task Learning
- Dependency Structure in Multi-task Learning with Gaussian Processes
- Symmetric Dependency Structure
- Predictive Mean for Symmetric Multi-task GP
- Asymmetric Dependency Structure
- Hyperparameter Learning
- Related Work and Discussion
- Examining the Generalisation Error for Asymmetric and Symmetric Models
- Generalisation Error for a Test Point x*
- Intuition about the Generalisation Errors
- Experiments
- Synthetic Data
- fMRI Data
- Conclusion
- References
- Reinforcement Learning through Global Stochastic Search in N-MDPs
- Introduction
- Background and Related Work
- Notation
- Previous Results for N-MDPs
- A Sound Local Algorithm
- The Algorithm: SoSMC
- Exploration: Gathering Information
- Assessment
- Exploration Strategies
- Experimental Evaluation
- Parr and Russell's Grid World
- Sutton's Grid World
- Keepaway
- Conclusions
- References
- Analyzing Word Frequencies in Large Text Corpora Using Inter-arrival Times and Bootstrapping
- Introduction
- Related Work
- Problem Setting
- Methods
- Method 1: Bernoulli Trials
- Method 2: Inter-arrival Times
- Method 3: Bootstrapping
- Experiments
- BNC: A Simple Benchmark
- BNC: Differences between Male and Female Authors
- BNC: Differences between the Main Genres
- SFCNC: Language Change over Time
- SFCNC: Locating Dates of Important Events
- Conclusion
- References
- Discovering Temporal Bisociations for Linking Concepts over Time
- Introduction
- Related Works and Contribution
- Formal Definition of the Problem
- Discovering Temporal Bisociations
- Check for Direct Connections
- Generation of Abstract Descriptions
- Linking Concepts over Time
- Experiments on Biomedical Literature
- Conclusions
- References
- Minimum Neighbor Distance Estimators of Intrinsic Dimension
- Introduction
- Related Works
- The Proposed Algorithms
- Base Theoretical Results
- Maximum Likelihood Approaches
- A pdf Comparison Approach
- Algorithm Evaluation
- Dataset Description
- Experimental Setting
- Experimental Results
- Conclusions and Future Works
- References
- Graph Evolution via Social Diffusion Processes
- Introduction
- Social Diffusion Process for Friendship Broadening
- Preliminaries
- Social Events and Broadening of Friendship
- Social Diffusion Process
- Graph Evolution Based on Social Diffusion Process
- The Evolution Algorithm
- Application of Graph Evolution
- Experimental Results
- Convergence Analysis
- Clustering
- Semi-supervised Learning
- Graph Evolution for microRNA Functionality Analysis
- Conclusions
- References
- Multi-Subspace Representation and Discovery
- Introduction
- Problem Description and Our Solution
- Multi-Subspace Discovery Problem
- A Constructive Solution
- Multi-Subspace Representation with Noise
- Multi-Subspace Representation
- Relation to Previous Work
- An Efficient Algorithm and Analysis
- Outline of the Algorithm
- Optimization Algorithm
- Theoretical Analysis of Algorithm 1
- Applications
- Using Multi-Subspace Representation as Preprocessing
- Using Multi-Subspace Representation as Classifier
- Experiment
- A Toy Example
- Experimental Settings
- Experimental Results
- Conclusions
- References
- A Novel Stability Based Feature Selection Framework for k-means Clustering
- Introduction
- Spectral k-means
- Stable Sparse PCA
- Stability Maximizing Objective and the Cluster Separation/Variance Tradeoff
- Two-Way Stability
- Optimization Framework
- Useful Bounds for Optimizing Stability
- Greedy Solutions
- Efficient Deflation for Multiple Clusters
- Related Work
- Experiments
- Conclusions and Further Work
- References
- Link Prediction via Matrix Factorization
- The Link Prediction Problem
- Challenges in Link Prediction
- Our Contributions
- Problem Definition and Notation
- Existing Link Prediction Models
- Do Existing Methods Meet the Challenges in Link Prediction?
- Extending Matrix Factorization for Link Prediction
- Why is the Factorization Approach Appealing?
- How Do We Combine Explicit and Latent Features?
- How Do We Overcome Imbalance?
- The Final Model
- Experimental Design
- Experimental Results
- Do latent features improve on unsupervised scores?
- Conclusion
- References
- On Oblique Random Forests
- Introduction
- Oblique Random Forests
- Comparison of Classification Performances
- Advantages of Oblique Model Trees
- Feature Importance and Sample Proximity
- Conclusion
- References
- An Alternating Direction Method for Dual MAP LP Relaxation
- Introduction
- MAP and LP Relaxation
- The Alternating Direction Method of Multipliers
- The Augmented Dual LP Algorithm
- Experimental Results
- Discussion
- References
- Aggregating Independent and Dependent Models to Learn Multi-label Classifiers
- Introduction
- Notation and Related Work
- Formal Framework for Multi-label Classification
- Some Approaches for Multi-label Classification
- Aggregating Independent and Dependent Classifiers
- Comparison with Related Approaches
- Experiments
- AID Classifier vs. Stacking Approach
- AID Classifier vs. State-of-the-Art Methods
- Conclusions
- References
- Tensor Factorization Using Auxiliary Information
- Introduction
- Tensor Completion Problem with Auxiliary Information
- Tensor Analysis Using Low-Rank Decomposition
- Tensor Completion with Auxiliary Information
- Proposed Methods: Within-Mode and Cross-Mode Regularization
- Regularization Using Auxiliary Similarity Matrices
- Method 1: Within-Mode Regularization
- Proposed Method 2: Cross-Mode Regularization
- Experiments
- Datasets
- Experimental Settings
- Results
- Related Work
- Conclusion and Future Work
- References
- Kernels for Link Prediction with Latent Feature Models
- Introduction
- Latent Feature Models of Graphs
- Biological Motivation
- Latent Feature Models of Graphs
- Ideal Kernels
- Link Kernels with Latent Features
- Node Kernels with Latent Features
- Relation to Ideal Kernels on Sparse Graphs
- Link Kernels with Latent Features
- Demonstration
- Application on Non-similarity Networks with Latent Features
- Latent Feature versus Similarity
- Execution Time
- Link Prediction Results
- Comparison to Sequence-Based Prediction
- Conclusion
- References
- Frequency-Aware Truncated Methods for Sparse Online Learning
- Introduction
- Linear Sparse Online Supervised Learning
- Problem Setting
- Related Works
- Frequency-Aware Truncated Methods
- Subgradient Method with Frequency-Aware Truncation
- Regret Analysis of SGFT
- Lazy Update
- SGFT with Cumulative Penalty
- Evaluation
- Conclusion
- References
- A Shapley Value Approach for Influence Attribution
- Introduction
- Related Work
- Problem Setting
- Example: Author-Publication Instantiation
- Methods
- Naïve Approach
- The Shapley Value Approach
- The Iterative Algorithm
- Enforcing Monotonicity of the Gain Function
- Experiments
- Setup
- Experimental Results
- Conclusions
- References
- Fast Approximate Text Document Clustering Using Compressive Sampling
- Introduction
- Coherence and Random Projections
- Sampling Cyclic Signals
- Sampling Sparse Signals
- Compressive Clustering
- Document Clustering
- Complex Radial K-means
- Approximate k-means Document Clustering
- Performance
- Cluster Accuracy
- Radial K-means without Sampling
- Radial K-means with DFT Sampling
- Radial K-means with DCT Sampling
- Clustering Large Scale Document Sets
- Related Work
- Conclusion
- References
- Ancestor Relations in the Presence of Unobserved Variables
- Introduction
- Bayesian Discovery of Ancestor Relations
- Computation
- Experiments
- Challenges of Learning Ancestor Relations
- A Simulation Study
- Real Life Data
- Discussion
- References
- ShareBoost: Boosting for Multi-view Learningwith Performance Guarantees
- Introduction
- Related Work
- Shared Sampling Algorithm
- Randomized Shared Sampling Algorithm
- Adversarial Multi-armed Bandit Approach
- Exp3.P: Exponential-Weight Algorithm for Exploration and Exploitation
- Randomized ShareBoost: Combining ShareBoost and Exp3.P
- Convergence Analysis of Randomized ShareBoost
- Experiments
- Summary
- References
- Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains
- Introduction
- Background
- Partially Observable Markov Decision Processes
- Planning as Inference
- State Splitting
- Local Optima Analysis
- Escaping Local Optima
- Forward Search
- Node Splitting
- Computational Complexity
- Experiments
- Conclusion
- References
- Abductive Plan Recognition by Extending Bayesian Logic Programs
- Introduction
- Background
- Logical Abduction
- Bayesian Logic Programs
- Bayesian Abductive Logic Programs
- Logical Abduction
- Probabilistic Parameters and Inference
- Parameter Learning
- Experimental Evaluation
- Datasets
- Comparison with Other Approaches
- Parameter Learning Experiments
- Discussion
- Related Work
- Future Work
- Conclusions
- References
- Higher Order Contractive Auto-Encoder
- Introduction
- Considered Framework
- Setup and Notation
- Basic Auto-Encoder
- The First-Order Contractive Auto-Encoder
- Proposed Higher Order Regularization
- Geometric Interpretation
- Related Previous Work
- Experiments
- Analysis of CAE+H
- Experimental Results
- MNIST Variants
- CIFAR-10
- Discussion
- References
- The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling
- Introduction
- Related Work
- Preliminaries
- The VC-Dimension of Classes of Queries
- Implementation
- Experiments
- Conclusions
- References
- Author Index
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