In this paper, we propose a low-rank representation with symmetric constraint
(LRRSC) method for robust subspace clustering. Given a collection of data
points approximately drawn from multiple subspaces, the proposed technique can
simultaneously recover the dimension and members of each subspace. LRRSC
extends the original low-rank representation algorithm by integrating a
symmetric constraint into the low-rankness property of high-dimensional data
representation. The symmetric low-rank representation, which preserves the
subspace structures of high-dimensional data, guarantees weight consistency for
each pair of data points so that highly correlated data points of subspaces are
represented together. Moreover, it can be efficiently calculated by solving a
convex optimization problem. We provide a rigorous proof for minimizing the
nuclear-norm regularized least square problem with a symmetric constraint. The
affinity matrix for spectral clustering can be obtained by further exploiting
the angular information of the principal directions of the symmetric low-rank
representation. This is a critical step towards evaluating the memberships
between data points. Experimental results on benchmark databases demonstrate
the effectiveness and robustness of LRRSC compared with several
state-of-the-art subspace clustering algorithms.Comment: 12 page
We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative representation combined with low-rank matrix recovery techniques as a low-rank representation to learn a symmetric low-rank representation, which preserves the subspace structures of high-dimensional data. In contrast to performing iterative singular value decomposition in some existing low-rank representation based algorithms, the symmetric low-rank representation in the SLRR method can be calculated as a closed form solution by solving the symmetric low-rank optimization problem. By making use of the angular information of the principal directions of the symmetric low-rank representation, an affinity graph matrix is constructed for spectral clustering. Extensive experimental results show that it outperforms state-of-the-art subspace clustering algorithms.
Abstract. With the development of X-ray, CT, MRI and other medical imaging techniques, doctors and researchers are provided with a large number of medical images for clinical diagnosis. It can largely improves the accuracy and reliability of disease diagnosis. In this paper, the method of brain CT image classification with Deep neural networks is proposed. Deep neural network exploits many layers of non-linear information for classification and pattern analysis. In the most recent literature, deep learning is defined as a kind of representation learning, which involves a hierarchy architecture where higher-level concepts are constructed from lower-level ones. The techniques developed from deep learning, enriched the main research aspects of machine learning and artificial intelligence, have already been impacting a wide range of signal and information processing researches. By using the normal and abnormal brain CT images, texture features are extracted as the characteristic value of each image. Then, deep neural network is used to realize the CT image classification of brain health. Experimental results indicate that the deep neural network have performed well in the CT images classification of brain health. It also shows that the stability of the network increases significantly as the depth of the network increasing.
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