21,099 research outputs found
Unsupervised spectral sub-feature learning for hyperspectral image classification
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods
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
Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
With the increasing number of deep multi-wavelength galaxy surveys, the
spectral energy distribution (SED) of galaxies has become an invaluable tool
for studying the formation of their structures and their evolution. In this
context, standard analysis relies on simple spectro-photometric selection
criteria based on a few SED colors. If this fully supervised classification
already yielded clear achievements, it is not optimal to extract relevant
information from the data. In this article, we propose to employ very recent
advances in machine learning, and more precisely in feature learning, to derive
a data-driven diagram. We show that the proposed approach based on denoising
autoencoders recovers the bi-modality in the galaxy population in an
unsupervised manner, without using any prior knowledge on galaxy SED
classification. This technique has been compared to principal component
analysis (PCA) and to standard color/color representations. In addition,
preliminary results illustrate that this enables the capturing of extra
physically meaningful information, such as redshift dependence, galaxy mass
evolution and variation over the specific star formation rate. PCA also results
in an unsupervised representation with physical properties, such as mass and
sSFR, although this representation separates out. less other characteristics
(bimodality, redshift evolution) than denoising autoencoders.Comment: 11 pages and 15 figures. To be published in A&
Feature Selection for Linear SVM with Provable Guarantees
We give two provably accurate feature-selection techniques for the linear
SVM. The algorithms run in deterministic and randomized time respectively. Our
algorithms can be used in an unsupervised or supervised setting. The supervised
approach is based on sampling features from support vectors. We prove that the
margin in the feature space is preserved to within -relative error of
the margin in the full feature space in the worst-case. In the unsupervised
setting, we also provide worst-case guarantees of the radius of the minimum
enclosing ball, thereby ensuring comparable generalization as in the full
feature space and resolving an open problem posed in Dasgupta et al. We present
extensive experiments on real-world datasets to support our theory and to
demonstrate that our method is competitive and often better than prior
state-of-the-art, for which there are no known provable guarantees.Comment: Appearing in Proceedings of 18th AISTATS, JMLR W&CP, vol 38, 201
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