Results 51 to 60 of about 63,767 (263)
Sample Complexity of Dictionary Learning and other Matrix Factorizations [PDF]
, 2015 Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by ...
Bach, Francis +4 more
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Journal of King Saud University: Computer and Information Sciences, 2019 Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons.
Mahin Vazifehdan +2 more
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Discriminant Nonnegative Tensor Factorization Algorithms [PDF]
IEEE Transactions on Neural Networks, 2009 Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis, especially for object representation and recognition. NMF requires the object tensor (with valence more than one) to be vectorized. This procedure may result in information loss since the local object structure is lost due to vectorization. Recently, in order to
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Rank Determination in Tensor Factor Model [PDF]
SSRN Electronic Journal, 2020 Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance and statistics. This paper develops two criteria for the determination of the number of factors for tensor factor models where the signal part of an observed tensor time series assumes a Tucker decomposition
Han, Yuefeng, Chen, Rong, Zhang, Cun-Hui
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The Scientific World Journal, 2014 In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm.
Zhang XiuJun, Liu Chang
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, 2023 We consider (robust) inference in the context of a factor model for tensor-valued sequences. We study the consistency of the estimated common factors and loadings space when using estimators based on minimising quadratic loss functions. Building on the observation that such loss functions are adequate only if sufficiently many moments exist, we extend ...
Barigozzi, Matteo +3 more
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Journal of King Saud University: Computer and Information Sciences, 2019 Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis.
Atefeh Nekouie, Mohammad Hossein Moattar
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Robust Tensor Factorization for Color Image and Grayscale Video Recovery
IEEE Access, 2020 Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du +4 more
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Efficient tensor completion for color image and video recovery: Low-rank tensor train
, 2016 This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its ...
Bengua, Johann A. +3 more
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, 2018 Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor.
Li, Lexin, Sun, Will Wei
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