3,068 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
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
Projection algorithms such as t-SNE or UMAP are useful for the visualization
of high dimensional data, but depend on hyperparameters which must be tuned
carefully. Unfortunately, iteratively recomputing projections to find the
optimal hyperparameter value is computationally intensive and unintuitive due
to the stochastic nature of these methods. In this paper we propose HyperNP, a
scalable method that allows for real-time interactive hyperparameter
exploration of projection methods by training neural network approximations.
HyperNP can be trained on a fraction of the total data instances and
hyperparameter configurations and can compute projections for new data and
hyperparameters at interactive speeds. HyperNP is compact in size and fast to
compute, thus allowing it to be embedded in lightweight visualization systems
such as web browsers. We evaluate the performance of the HyperNP across three
datasets in terms of performance and speed. The results suggest that HyperNP is
accurate, scalable, interactive, and appropriate for use in real-world
settings
Visualizing the Hidden Activity of Artificial Neural Networks.
In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles
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