5,968 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
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Despite the recent progress, the existing multi-view unsupervised feature
selection methods mostly suffer from two limitations. First, they generally
utilize either cluster structure or similarity structure to guide the feature
selection, neglecting the possibility of a joint formulation with mutual
benefits. Second, they often learn the similarity structure by either global
structure learning or local structure learning, lacking the capability of graph
learning with both global and local structural awareness. In light of this,
this paper presents a joint multi-view unsupervised feature selection and graph
learning (JMVFG) approach. Particularly, we formulate the multi-view feature
selection with orthogonal decomposition, where each target matrix is decomposed
into a view-specific basis matrix and a view-consistent cluster indicator.
Cross-space locality preservation is incorporated to bridge the cluster
structure learning in the projected space and the similarity learning (i.e.,
graph learning) in the original space. Further, a unified objective function is
presented to enable the simultaneous learning of the cluster structure, the
global and local similarity structures, and the multi-view consistency and
inconsistency, upon which an alternating optimization algorithm is developed
with theoretically proved convergence. Extensive experiments demonstrate the
superiority of our approach for both multi-view feature selection and graph
learning tasks
Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural model
that discovers clusters of data points drawn from a union of low-dimensional
subspaces. In contrast to previous attempts, our model runs without the aid of
spectral clustering. This makes our algorithm one of the kinds that can
gracefully scale to large datasets. At its heart, our neural model benefits
from a classifier which determines whether a pair of points lies on the same
subspace or not. Essential to our model is the construction of two affinity
matrices, one from the classifier and the other from a notion of subspace
self-expressiveness, to supervise training in a collaborative scheme. We
thoroughly assess and contrast the performance of our model against various
state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201
A Novel K-means-based Feature Reduction
The aim of feature reduction is reduction of the size of data file, elimination of irrelevant features, and discovery of the effective data features for data analysis. Irrelevant data features can skew data analysis such as data clustering. Therefore, maintaining the data structure or data clusters must be taken into consideration in feature reduction. In this article, with regard to the success of k-means-based clustering methods, a feature reduction method is presented based on weighted k-means (wk-means). More specifically, firstly, data features are weighted using wk-means method. A feature with a high weight is not a better feature for clustering than a feature with a low weight, necessarily, and the weight of a feature only change feature range for better clustering. Then, by using a novel mathematical model, a group of weighted features with the least effect on data clusters are eliminated and the remaining features are selected. Contrary to sparse k-means method, the number of selected features can be determined explicitly by the user in our proposed method. Experimental results on four real datasets show that the accuracy of clusters obtained by wk-means after feature reduction by the proposed method is better than that of sparse k-means, PCA and LLE
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