In this paper, we exploit the advantages of tensor representations and propose a Supervised Multilinear Learning Model for regression. The model is based on the Canonical (CAN-DECOMP)/Parallel Factors (PARAFAC) decomposition of tensors of multiple modes and allows the simultaneous projection of an input tensor to more than one discriminative directions along each mode. These projection weights are obtained by optimizing a ϵ-insensitive loss functions which leads to generalized Support Tensor Regression (STR). The methods are validated on the problems of head pose estimation using real data from publicly available databases
This thesis illustrates connections between statistical models for tensors, introduces a novel linea...
We revisit a multidimensional varying-coecient model (VCM), by allowing re-gressor coecients to vary...
In this paper we introduce the literature on regression models with tensor variables and present a B...
In this paper, we exploit the advantages of tensor representations and propose a Supervised Multilin...
In this paper, we exploit the advantages of tensor representa-tions and propose a Supervised Multili...
In this paper, we exploit the advantages of tensorial representations and propose several tensor lea...
AbstractSupport vector machine (SVM) not only can be used for classification, can also be applied to...
Tensor representation is helpful to reduce the small sample size problem in discriminative subspace ...
In this work, we introduce a new generalized nonlinear tensor regression framework called kernel-bas...
Neuroimaging data often take the form of high dimensional arrays, also known as tensors. Addressing ...
Most of the existing learning algorithms take vectors as their input data. A function is then learne...
Large-scale neuroimaging studies have been collecting brain images of study individ-uals, which take...
Publisher Copyright: © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.Tens...
A key ingredient to improve the generalization of machine learning algorithms is to convey prior inf...
We present a new supervised tensor regression method based on multi-way array decompositions and ker...
This thesis illustrates connections between statistical models for tensors, introduces a novel linea...
We revisit a multidimensional varying-coecient model (VCM), by allowing re-gressor coecients to vary...
In this paper we introduce the literature on regression models with tensor variables and present a B...
In this paper, we exploit the advantages of tensor representations and propose a Supervised Multilin...
In this paper, we exploit the advantages of tensor representa-tions and propose a Supervised Multili...
In this paper, we exploit the advantages of tensorial representations and propose several tensor lea...
AbstractSupport vector machine (SVM) not only can be used for classification, can also be applied to...
Tensor representation is helpful to reduce the small sample size problem in discriminative subspace ...
In this work, we introduce a new generalized nonlinear tensor regression framework called kernel-bas...
Neuroimaging data often take the form of high dimensional arrays, also known as tensors. Addressing ...
Most of the existing learning algorithms take vectors as their input data. A function is then learne...
Large-scale neuroimaging studies have been collecting brain images of study individ-uals, which take...
Publisher Copyright: © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.Tens...
A key ingredient to improve the generalization of machine learning algorithms is to convey prior inf...
We present a new supervised tensor regression method based on multi-way array decompositions and ker...
This thesis illustrates connections between statistical models for tensors, introduces a novel linea...
We revisit a multidimensional varying-coecient model (VCM), by allowing re-gressor coecients to vary...
In this paper we introduce the literature on regression models with tensor variables and present a B...
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