NDLI: Sparse Compositional Local Metric Learning

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Foreword to the Applied Data Science: Invited Talks Track at KDD-2017

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

Learning Certifiably Optimal Rule Lists

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications

The Future of Data Integration

More than the Sum of its Parts: Building Domino Data Lab

The Future of Artificially Intelligent Assistants

Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks

Tripoles: A New Class of Relationships in Time Series Data

Luck is Hard to Beat: The Difficulty of Sports Prediction

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Three Principles of Data Science: Predictability, Stability and Computability

Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy

Unsupervised Network Discovery for Brain Imaging Data

Post Processing Recommender Systems for Diversity

Planning Bike Lanes based on Sharing-Bikes' Trajectories

Extremely Fast Decision Tree Mining for Evolving Data Streams

Industrial Machine Learning

Patient Subtyping via Time-Aware LSTM Networks

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform

Real-Time Optimization of Web Publisher RTB Revenues

Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management

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Bolt: Accelerated Data Mining with Fast Vector Compression

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Customer Lifetime Value Prediction Using Embeddings

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Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

A Data Science Approach to Understanding Residential Water Contamination in Flint

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

Planning and Learning under Uncertainty: Theory and Practice

A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Estimation of Recent Ancestral Origins of Individuals on a Large Scale

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices

Big Data in Climate: Opportunities and Challenges for Machine Learning

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Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments

Addressing Challenges with Big Data for Media Measurement

Fast Enumeration of Large k-Plexes

On Sampling Strategies for Neural Network-based Collaborative Filtering

A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem

Machine Learning Software in Practice: Quo Vadis?

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

Unsupervised Feature Selection in Signed Social Networks

FIRST: Fast Interactive Attributed Subgraph Matching

Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

Designing AI at Scale to Power Everyday Life

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Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning

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Sparse Compositional Local Metric Learning

Content Provider	ACM Digital Library
Author	St.Amand, Joseph Huan, Jun
Abstract	Mahalanobis distance metric learning becomes an especially challenging problem as the dimension of the feature space p is scaled upwards. The number of parameters to optimize grows with space complexity of order O (p $^{2}),$ making storage infeasible, interpretability poor, and causing the model to have a high tendency to overfit. Additionally, optimization while maintaining feasibility of the solution becomes prohibitively expensive, requiring a projection onto the positive semi-definite cone after every iteration. In addition to the obvious space and computational challenges, vanilla distance metric learning is unable to model complex and multi-modal trends in the data. Inspired by the recent resurgence of Frank-Wolfe style optimization, we propose a new method for sparse compositional local Mahalanobis distance metric learning. Our proposed technique learns a set of distance metrics which are composed of local and global components. We capture local interactions in the feature space, while ensuring that all metrics share a global component, which may act as a regularizer. We optimize our model using an alternating pairwise Frank-Wolfe style algorithm. This serves a dual purpose, we can control the sparsity of our solution, and altogether avoid any expensive projection operations. Finally, we conduct an empirical evaluation of our method with the current state of the art and present the results on five datasets from varying domains.
Starting Page	1097
Ending Page	1104
Page Count	8
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098153
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sparse metric learning Frank-wolfe optimization Mahalanobis distance
Content Type	Text
Resource Type	Article

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