27,681 research outputs found
Missing Value Imputation With Unsupervised Backpropagation
Many data mining and data analysis techniques operate on dense matrices or
complete tables of data. Real-world data sets, however, often contain unknown
values. Even many classification algorithms that are designed to operate with
missing values still exhibit deteriorated accuracy. One approach to handling
missing values is to fill in (impute) the missing values. In this paper, we
present a technique for unsupervised learning called Unsupervised
Backpropagation (UBP), which trains a multi-layer perceptron to fit to the
manifold sampled by a set of observed point-vectors. We evaluate UBP with the
task of imputing missing values in datasets, and show that UBP is able to
predict missing values with significantly lower sum-squared error than other
collaborative filtering and imputation techniques. We also demonstrate with 24
datasets and 9 supervised learning algorithms that classification accuracy is
usually higher when randomly-withheld values are imputed using UBP, rather than
with other methods
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned
dictionary has been the focus of much recent research in machine learning,
neuroscience and signal processing. For signals such as natural images that
admit such sparse representations, it is now well established that these models
are well suited to restoration tasks. In this context, learning the dictionary
amounts to solving a large-scale matrix factorization problem, which can be
done efficiently with classical optimization tools. The same approach has also
been used for learning features from data for other purposes, e.g., image
classification, but tuning the dictionary in a supervised way for these tasks
has proven to be more difficult. In this paper, we present a general
formulation for supervised dictionary learning adapted to a wide variety of
tasks, and present an efficient algorithm for solving the corresponding
optimization problem. Experiments on handwritten digit classification, digital
art identification, nonlinear inverse image problems, and compressed sensing
demonstrate that our approach is effective in large-scale settings, and is well
suited to supervised and semi-supervised classification, as well as regression
tasks for data that admit sparse representations.Comment: final draft post-refereein
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