8,280 research outputs found
Graph Laplacian for Semi-Supervised Learning
Semi-supervised learning is highly useful in common scenarios where labeled
data is scarce but unlabeled data is abundant. The graph (or nonlocal)
Laplacian is a fundamental smoothing operator for solving various learning
tasks. For unsupervised clustering, a spectral embedding is often used, based
on graph-Laplacian eigenvectors. For semi-supervised problems, the common
approach is to solve a constrained optimization problem, regularized by a
Dirichlet energy, based on the graph-Laplacian. However, as supervision
decreases, Dirichlet optimization becomes suboptimal. We therefore would like
to obtain a smooth transition between unsupervised clustering and
low-supervised graph-based classification. In this paper, we propose a new type
of graph-Laplacian which is adapted for Semi-Supervised Learning (SSL)
problems. It is based on both density and contrastive measures and allows the
encoding of the labeled data directly in the operator. Thus, we can perform
successfully semi-supervised learning using spectral clustering. The benefits
of our approach are illustrated for several SSL problems.Comment: 12 pages, 6 figure
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 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
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