8,354 research outputs found
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Efficient AUC Optimization for Information Ranking Applications
Adequate evaluation of an information retrieval system to estimate future
performance is a crucial task. Area under the ROC curve (AUC) is widely used to
evaluate the generalization of a retrieval system. However, the objective
function optimized in many retrieval systems is the error rate and not the AUC
value. This paper provides an efficient and effective non-linear approach to
optimize AUC using additive regression trees, with a special emphasis on the
use of multi-class AUC (MAUC) because multiple relevance levels are widely used
in many ranking applications. Compared to a conventional linear approach, the
performance of the non-linear approach is comparable on binary-relevance
benchmark datasets and is better on multi-relevance benchmark datasets.Comment: 12 page
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