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In this paper, we propose a novel categorization framework to recognize tactile sequences based on two particular properties of the tactile data. For the first one, tactile sequences are spatio-temporal data which is sequential and dynamic, depicting the process of grasping an object in different grasping stages; therefore, it is reasonable to discover the dynamical pattern by modeling tactile data...
Previous work has shown that perceptual texture similarity and relative attributes cannot be well described by computational features. In this paper, we propose to predict human's visual perception of texture images by learning a non-linear mapping from computational feature space to perceptual space. Hand-crafted features and deep features, which were successfully applied in texture classification...
Spectral clustering has shown a superior performance in analyzing the cluster structure. However, the exponentially computational complexity limits its application in analyzing large-scale data. To tackle this problem, many low-rank matrix approximating algorithms are proposed, of which the Nyström method is an approach with proved lower approximate errors. The algorithms commonly combine two powerful...
Matrix manifolds such as Stiefel and Grassmann manifolds have been widely used in modern computer vision. This paper is concerned with the problem of classifying such manifold-valued data, based on the maximum likelihood estimation for the parametric probability density functions defined on the manifolds. By using a new way of computing normalisation constants for the matrix Langevin distribution...
In this paper, a class of second-order nonlinear time-delayed multiagent systems with disturbance is investigated. In order to improve the adaptivity, neural networks are used to learn the unknown dynamics. Then, by utilizing Lyapunov-Krasovskii functional, time delays can be eliminated. Moreover, a robustifying term is introduced to constrain external disturbance. With divide-and-conquer idea, the...
In this research paper, Complex Recurrent Hopfield Networks are introduced. Dynamics of a Structured Complex Recurrent Hopfield Network whose synaptic weight matrix is skew Hermitian is studied. It is proved that such a network when operated in parallel mode leads to a cycle of length 4. Two new types of complex matrices known as Braided Hermitian and Braided Skew Hermitian are defined and dynamics...
We present a novel online metric learning model, called scalable large margin online metric learning (SLMOML). SLMOML belongs to the passive-aggressive family of learning models. In the formulation of SLMOML, we use the LogDet divergence to measure the closeness between two continuously learned matrices, which naturally ensures the positive semi-definiteness of the learned matrix at each iteration,...
Recently, models of neural networks in the real domain have been extended into the high dimensional domain such as the complex number and quaternion domain, and several high-dimensional models have been proposed. These extensions are generalized by introducing Clifford algebra (geometric algebra). In this paper we extend conventional real-valued models of recurrent neural networks into the octonion...
We show that simple linear classification of pairwise products of convolutional features achieves near state-of-the-art performance on some standard labelled image databases. Specifically, we found test classification error rates on the MNIST handwritten digits image database of under 0.5%, and achieved under 19% and under 44% error rates on the CIFAR-10 and CIFAR-100 RGB image databases. Since the...
Multi-label learning is popular in current research of machine learning areas, and there have already been many methods using label relationship to solve multi-label problems. However, the meaning of their relationship is not so obvious that it's hard for us to know the fact among labels. Besides, with the development of multi-label learning, hierarchical multi-label classification is a new research...
Detecting overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complex detection have been developed for PPI networks. However, the majority of algorithms primarily depend on network topological features and/or gene expression...
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