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Subspace Discrimination for Multiway Data
Sampled values of volumetric data are expressed as third-order tensors. Object-oriented data analysis requires us to process and analyse volumetric...
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Multilinear subspace learning using handcrafted and deep features for face kinship verification in the wild
In this paper, we propose a new multilinear and multiview subspace learning method called Tensor Cross-view Quadratic Discriminant Analysis for face...
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Hypergraph regularized low-rank tensor multi-view subspace clustering via L1 norm constraint
In order to mine the manifold information in different views, a hypergraph regularized low-rank tensor multi-view subspace clustering via L1 norm...
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Tensor Train Subspace Analysis for Classification of Hand Gestures with Surface EMG Signals
Processing and classification of surface EMG signals is a challenging computational problem that has received increasing attention for at least two...
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A novel descriptor (LGBQ) based on Gabor filters
Recently, many existing automatic facial verification methods have focused on learning the optimal distance measurements between facials. Especially...
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A new multidimensional discriminant representation for robust person re-identification
Person Re-Identification (PRe-ID) or person retrieval is a challenging task of computer vision, aiming to identify a specific person across disjoint...
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Multi-view subspace clustering with adaptive locally consistent graph regularization
Graph regularization has shown its effectiveness in multi-view subspace clustering methods. Many multi-view subspace clustering methods based on...
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Theories, algorithms and applications in tensor learning
Due to the accelerated development and popularization of Internet, mobile Internet, and Internet of Things and the breakthrough of storage and...
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Learning multi-tasks with inconsistent labels by using auxiliary big task
Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly...
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Adversarial Defense Mechanisms for Supervised Learning
In this chapter we explore neural network architectures, implementations, cost analysis, and training processes using game theoretical adversarial...
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A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis
Rotatingmachinery is the main component of mechanical equipment. Nevertheless, due to variation of operating condition results in important detection...
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A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach
This article uses an anonymous 2014–15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the...
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Canonical polyadic decomposition (CPD) of big tensors with low multilinear rank
Tensor decomposition methods have been widely applied to big data analysis as they bring multiple modes and aspects of data to a unified framework,...
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A Krylov-Schur-like method for computing the best rank-(r1,r2,r3) approximation of large and sparse tensors
The paper is concerned with methods for computing the best low multilinear rank approximation of large and sparse tensors. Krylov-type methods have...
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Polynomial-Time Cryptanalysis of the Subspace Flooding Assumption for Post-quantum \(i\mathcal {O}\)
Indistinguishability Obfuscation \((i\mathcal {O})\)...
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Multimodal 2d + 3d multi-descriptor tensor for face verification
In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this...
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Deep Learning
Machine learning techniques have always played an important and often central role in the development of computer vision algorithms.
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Adaptive graph regularized non-negative Tucker decomposition for multiway dimensionality reduction
Non-negative Tucker decomposition (NTD) is a powerful tool for data representation to capture rich internal structure information from non-negative...
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Towards efficient and accurate approximation: tensor decomposition based on randomized block Krylov iteration
Tensor decomposition methods are inefficient when dealing with low-rank approximation of large-scale data. Randomized tensor decomposition has...
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Continual learning classification method and its application to equipment fault diagnosis
Classification methods play a significant role in the fault diagnosis field. However, they cannot effectively recognize new types of fault data and...
