2,108 research outputs found
Semi-supervised Learning based on Distributionally Robust Optimization
We propose a novel method for semi-supervised learning (SSL) based on
data-driven distributionally robust optimization (DRO) using optimal transport
metrics. Our proposed method enhances generalization error by using the
unlabeled data to restrict the support of the worst case distribution in our
DRO formulation. We enable the implementation of our DRO formulation by
proposing a stochastic gradient descent algorithm which allows to easily
implement the training procedure. We demonstrate that our Semi-supervised DRO
method is able to improve the generalization error over natural supervised
procedures and state-of-the-art SSL estimators. Finally, we include a
discussion on the large sample behavior of the optimal uncertainty region in
the DRO formulation. Our discussion exposes important aspects such as the role
of dimension reduction in SSL
Dropout Training as Adaptive Regularization
Dropout and other feature noising schemes control overfitting by artificially
corrupting the training data. For generalized linear models, dropout performs a
form of adaptive regularization. Using this viewpoint, we show that the dropout
regularizer is first-order equivalent to an L2 regularizer applied after
scaling the features by an estimate of the inverse diagonal Fisher information
matrix. We also establish a connection to AdaGrad, an online learning
algorithm, and find that a close relative of AdaGrad operates by repeatedly
solving linear dropout-regularized problems. By casting dropout as
regularization, we develop a natural semi-supervised algorithm that uses
unlabeled data to create a better adaptive regularizer. We apply this idea to
document classification tasks, and show that it consistently boosts the
performance of dropout training, improving on state-of-the-art results on the
IMDB reviews dataset.Comment: 11 pages. Advances in Neural Information Processing Systems (NIPS),
201
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
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