Results 41 to 50 of about 12,418,444 (305)
Sequential low-rank change detection [PDF]
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016 Detecting emergence of a low-rank signal from high-dimensional data is an important problem arising from many applications such as camera surveillance and swarm monitoring using sensors. We consider a procedure based on the largest eigenvalue of the sample covariance matrix over a sliding window to detect the change. To achieve dimensionality reduction,
Xie, Yao, Seversky, Lee
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Bounded Matrix Low Rank Approximation [PDF]
2012 IEEE 12th International Conference on Data Mining, 2015 Low rank approximation is the problem of finding two matrices P∈Rm×k and Q∈Rk×n for input matrix R∈Rm×n, such that R≈PQ. It is common in recommender systems rating matrix, where the input matrix R is bounded in the closed interval [rmin,rmax] such as [1, 5].
Ramakrishnan Kannan +3 more
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Distributed Low-Rank Subspace Segmentation [PDF]
2013 IEEE International Conference on Computer Vision, 2013 Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional subspaces from noisy and corrupted input data.
Talwalkar, Ameet +4 more
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Robust Generalized Low Rank Approximations of Matrices. [PDF]
PLoS ONE, 2015 In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries.
Jiarong Shi, Wei Yang, Xiuyun Zheng
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Low-Rank Modeling and Its Applications in Image Analysis [PDF]
, 2014 Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and ...
Yang, Can +3 more
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The Annals of Statistics, 2023 This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has rank that grows slowly compared to its dimensions and nonzero singular values that diverge to infinity.
Chernozhukov, Victor +3 more
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Manifold Constrained Low-Rank Decomposition
, 2017 Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from ...
Chen, Chen +3 more
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Decomposition of homogeneous polynomials with low rank [PDF]
, 2010 Let $F$ be a homogeneous polynomial of degree $d$ in $m+1$ variables defined over an algebraically closed field of characteristic zero and suppose that $F$ belongs to the $s$-th secant varieties of the standard Veronese variety $X_{m,d}\subset \mathbb{P}^
A. Bernardi +15 more
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IEEE Access, 2023 Recognizing the multiple categories of an high-resolution (HR) aerial photos is an indispensable technique in geoscience and remote sensing. In this work, a perceptual low-rank algorithm combined with a geometry-preserving feature selection (FS) is ...
Junwu Zhou, Fuji Ren
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Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
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