Results 101 to 110 of about 459,246 (134)

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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Adaptive Low Rank Matrix Completion

IEEE Transactions on Signal Processing, 2017
The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices ...
Ruchi Tripathi, Boda Mohan, Ketan Rajawat   +2 more
openaire   +1 more source

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, Xiaozhao Fang, Yong Xu, Chunwei Tian, Lunke Fei   +4 more
openaire   +3 more sources

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 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, Bob Zhang, Yong Xu, Jian Yang, Na Han   +4 more
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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
openaire   +1 more source

Low-Rank Matrix Factorization With Adaptive Graph Regularizer

IEEE Transactions on Image Processing, 2016
In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix ...
Gui-Fu, Lu, Yong, Wang, Jian, Zou
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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
openaire   +1 more source

Subspace Clustering via Adaptive Low-Rank Model

2017
Subspace Clustering has been a major issue in many real-world task and sparse and low-rank representation based methods have received considerable attention during the past decades. However, both above methods need huge computation in order to solve sparse or trace-norm minimization problem, which may not be scalable to large-scale data. In this paper,
Mingbo Zhao, Wenlong Cheng, Zhao Zhang, Choujun Zhan   +3 more
openaire   +1 more source