Results 71 to 80 of about 223,331 (198)
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
, 2022 International audienceWe study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear and certain type of non-linear neural networks ...
Hariz, Kais +4 more
core
Using underapproximations for sparse nonnegative matrix factorization [PDF]
Nonnegative Matrix Factorization (NMF) has gathered a lot of attention in the last decade and has been successfully applied in numerous applications.
GILLIS, Nicolas, GLINEUR, François
core
Semantic Data Representation for Improving Tensor Factorization
, 2014 Predicting human activities is important for improving recommender systems or analyzing social relationships among users. Those human activities are usually repre- sented as multi-object relationships (e.g.
Sawada, Hiroshi +11 more
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Nonnegative factorization and the maximum edge biclique problem [PDF]
Nonnegative matrix factorization (NMF) is a data analysis technique based on the approximation of a nonnegative matrix with a product of two nonnegative factors, which allows compression and interpretation of nonnegative data. In this paper, we study the
GILLIS, Nicolas, GLINEUR, François
core
Community-activity structure detection via non-negative tensor factorization.
, 2014 The original temporal network is represented as a three-way tensor, which is then decomposed by using non-negative tensor factorization. The complexity of the model (number of components ) is tuned by using quality indicators that provide information on ...
André Panisson (518215) +2 more
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A proximal ANLS algorithm for nonnegative tensor factorization with a periodic enhanced line search [PDF]
, 1999 summary:The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method.
Bunker, Douglas +3 more
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Unique tensor factorization of algebras
Mathematische Annalen, 1999 By an algebra the author means a finite-dimensional, associative, unitary \(\mathbb{C}\)-algebra, where \(\mathbb{C}\) is the field of complex numbers. An algebra \(A\) is called \(\oplus\)-indecomposable if in each direct decomposition \(A=A_1\oplus A_2\) exactly one of the summands is trivial, i.e. \(A_i=0\). Similarly an algebra is called \(\otimes\)
openaire +3 more sources
Algorithms for probabilistic latent tensor factorization
Signal Processing, 2012 We propose a general probabilistic framework for modelling multiway data. Our approach establishes a novel link between graphical representation of probability measures and tensor factorization models that allow us to design arbitrary tensor factorization models while retaining simplicity.
Yusuf Kenan Yilmaz, A. Taylan Cemgil
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Controlling sparseness in nonnegative tensor factorization
, 2006 Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001).
Christoph Schnörr, Matthias Heiler
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Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification
Remote Sensing, 2019 Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology.
Zhaohui Xue +3 more
doaj +1 more source