Results 21 to 30 of about 244,754 (100)
Directional clustering through matrix factorization [PDF]
, 2016 This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
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Blind Audio Source Separation Using Nonnegative Tensor Factorization Techniques [PDF]
, 2008 Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada, kokteyl partisi problemi olarak da bilinen, gözü kapalı ses işareti ...
Keyder, Mehmet Altuğ
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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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Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation [PDF]
, 2022 Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold ...
Tang, Linruize +3 more
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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
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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
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Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data
, 2020 Continuous advancement of brain imaging techniques has witnessed data analysis methods evolving from matrix component analysis to tensor component analysis, from individual analysis to group analysis regarding the analysis of brain data with multi-set/
Wang, Xiulin +4 more
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Theorems on positive data: on the uniqueness of NMF. [PDF]
, 2008 We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not.
Plumbley, MD +9 more
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, 2011 International audienceSeparating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information ...
Cedric Fevotte +7 more
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NIMFA: A Python Library for Nonnegative Matrix Factorization [PDF]
, 2012 NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring.
Zupan, Blaz, Zitnik, Marinka
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