Results 231 to 240 of about 173,284 (257)

Low-rank adaptive filters

IEEE Transactions on Signal Processing, 1996
We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory ...
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Adaptive Low Rank Approximation for Tensors

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015
In this paper, we propose a novel framework for finding low rank approximation of a given tensor. This framework is based on the adaptive lasso with coefficient weights for sparse computation in tensor rank detection. We also provide an algorithm for solving the adaptive lasso model problem for tensor approximation.
Xiaofei Wang, Carmeliza Navasca
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Adaptive weighted nonnegative low-rank representation

Pattern Recognition, 2018
Abstract Conventional graph based clustering methods treat all features equally even if they are redundant features or noise in the stage of graph learning, which is obviously unreasonable. In this paper, we propose a novel graph learning method named adaptive weighted nonnegative low-rank representation (AWNLRR) for data clustering.
Jie Wen 0001, Bob Zhang 0001, Yong Xu 0001, Jian Yang 0003, Na Han   +4 more
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Adaptive Metric Learning with the Low Rank Constraint

Proceedings of the International Conference on Internet Multimedia Computing and Service, 2016
Good quality distance metrics can significantly promote the performance of many computer vision applications. In order to learn an appropriate distance metric, most of existing metric learning approaches restrict the learned distances between similar pairs to be smaller than a given lower bound, while the learned distances between dissimilar pairs are ...
Yuan Fang, Yan Yan 0001, Hanzi Wang, Si Chen 0002, Xinbo Gao 0001   +4 more
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Low rank transform domain adaptive filtering

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136), 2002
This paper introduces a least squares, matrix based framework for adaptive filtering that includes Normalized LMS, Affine Projection, and Recursive Least Squares as special cases. We then introduce other transform domain based methods and show how to create optimal low rank versions. We also discuss efficient implementation of our method.
Linebarger, Darel, Raghothaman, Balaji, Begušić, Dinko, Dowling, Eric, DeGroat, Ronald, Oh, Stephen   +5 more
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Low-rank representation with adaptive graph regularization

Neural Networks, 2018
Low-rank representation (LRR) has aroused much attention in the community of data mining. However, it has the following twoproblems which greatly limit its applications: (1) it cannot discover the intrinsic structure of data owing to the neglect of the local structure of data; (2) the obtained graph is not the optimal graph for clustering. To solve the
Jie Wen 0001, Xiaozhao Fang, Yong Xu 0001, Chunwei Tian, Lunke Fei   +4 more
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Adaptive Low-Rank Gradient Descent

2023 62nd IEEE Conference on Decision and Control (CDC), 2023
Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh   +2 more
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Scale Regularization for Stable Low-Rank Adaptation

Proceedings of the AAAI Conference on Artificial Intelligence
Low-Rank Adaptation (LoRA) has emerged as a practical and efficient method for fine-tuning large language models under limited computational budgets. However, recent studies have shown that LoRA can suffer from training instability when applied to models with large embedding dimensions, due to the imbalanced in magnitudes between its low-rank matrices.
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Deep Robust Low Rank Correlation With Unifying Clustering Structure for Cross Domain Adaptation

IEEE Transactions on Multimedia, 2023
Xiang-Jun Shen, Yanan Cai, Stanley Ebhohimhen Abhadiomhen   +2 more
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Robust multi-source adaptation visual classification using supervised low-rank representation

Pattern Recognition, 2017
Jianwen Tao, Dawei Song
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