37,590 research outputs found
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Major complications arise from the recent increase in the amount of
high-dimensional data, including high computational costs and memory
requirements. Feature selection, which identifies the most relevant and
informative attributes of a dataset, has been introduced as a solution to this
problem. Most of the existing feature selection methods are computationally
inefficient; inefficient algorithms lead to high energy consumption, which is
not desirable for devices with limited computational and energy resources. In
this paper, a novel and flexible method for unsupervised feature selection is
proposed. This method, named QuickSelection, introduces the strength of the
neuron in sparse neural networks as a criterion to measure the feature
importance. This criterion, blended with sparsely connected denoising
autoencoders trained with the sparse evolutionary training procedure, derives
the importance of all input features simultaneously. We implement
QuickSelection in a purely sparse manner as opposed to the typical approach of
using a binary mask over connections to simulate sparsity. It results in a
considerable speed increase and memory reduction. When tested on several
benchmark datasets, including five low-dimensional and three high-dimensional
datasets, the proposed method is able to achieve the best trade-off of
classification and clustering accuracy, running time, and maximum memory usage,
among widely used approaches for feature selection. Besides, our proposed
method requires the least amount of energy among the state-of-the-art
autoencoder-based feature selection methods.Comment: 29 page
Unsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic structures are unreliable/inaccurate when the redundant and
noisy features are not removed. Therefore, we face a dilemma here: one need the
true structures of data to identify the informative features, and one need the
informative features to accurately estimate the true structures of data. To
address this, we propose a unified learning framework which performs structure
learning and feature selection simultaneously. The structures are adaptively
learned from the results of feature selection, and the informative features are
reselected to preserve the refined structures of data. By leveraging the
interactions between these two essential tasks, we are able to capture accurate
structures and select more informative features. Experimental results on many
benchmark data sets demonstrate that the proposed method outperforms many state
of the art unsupervised feature selection methods
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