Results 51 to 60 of about 13,997,719 (325)
Smooth Non-negative Low-Rank Graph Representation for Clustering [PDF]
Jisuanji kexue yu tansuoThe existing low-rank graph representation algorithms fail to capture the global representation structure of data accurately, and cannot make full use of the valid information of data to guide the construction of the representation graph, then the ...
QIAN Luoxiong, CHEN Mei, ZHANG Chi, ZHANG Jinhong, MA Xueyan
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, 2021 We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of well-known methods for it, including vector auto-regressive models, state-space methods, multi-task regression, and ...
Barratt, Shane +2 more
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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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Non-Negative Low Rank Graph Embedding Algorithm
Jisuanji kexue yu tansuo, 2020 The existing non-negative matrix factorization (NMF) algorithms still have some shortcomings. On one hand, the NMF method calculates its low-dimensional representation directly on the high-dimensional original image data set, but in fact the effective ...
LIU Guoqing, LU Guifu, ZHOU Sheng, XUAN Dongdong, CAO Along
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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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Compressing Transformers: Features Are Low-Rank, but Weights Are Not!
AAAI Conference on Artificial Intelligence, 2023 Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks, but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings.
Hao Yu, Jianxin Wu
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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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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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