8 research outputs found
Online low-rank representation learning for joint multi-subspace recovery and clustering
Benefiting from global rank constraints, the lowrank
representation (LRR) method has been shown to be an
effective solution to subspace learning. However, the global
mechanism also means that the LRR model is not suitable for
handling large-scale data or dynamic data. For large-scale data,
the LRR method suffers from high time complexity, and for
dynamic data, it has to recompute a complex rank minimization
for the entire data set whenever new samples are dynamically
added, making it prohibitively expensive. Existing attempts to
online LRR either take a stochastic approach or build the
representation purely based on a small sample set and treat
new input as out-of-sample data. The former often requires
multiple runs for good performance and thus takes longer time
to run, and the latter formulates online LRR as an out-ofsample
classification problem and is less robust to noise. In
this paper, a novel online low-rank representation subspace
learning method is proposed for both large-scale and dynamic
data. The proposed algorithm is composed of two stages: static
learning and dynamic updating. In the first stage, the subspace
structure is learned from a small number of data samples. In
the second stage, the intrinsic principal components of the entire
data set are computed incrementally by utilizing the learned
subspace structure, and the low-rank representation matrix can
also be incrementally solved by an efficient online singular value
decomposition (SVD) algorithm. The time complexity is reduced
dramatically for large-scale data, and repeated computation is
avoided for dynamic problems. We further perform theoretical
analysis comparing the proposed online algorithm with the batch
LRR method. Finally, experimental results on typical tasks
of subspace recovery and subspace clustering show that the
proposed algorithm performs comparably or better than batch
methods including the batch LRR, and significantly outperforms
state-of-the-art online methods
A new sparse representation framework for compressed sensing MRI
Abstract(#br)Compressed sensing based Magnetic Resonance imaging (MRI) via sparse representation (or transform) has recently attracted broad interest. The tight frame (TF)-based sparse representation is a promising approach in compressed sensing MRI. However, the conventional TF-based sparse representation is difficult to utilize the sparsity of the whole image. Since the whole image usually has different structure textures and a kind of tight frame can only represent a particular kind of ground object, how to reconstruct high-quality of magnetic resonance (MR) image is a challenge. In this work, we propose a new sparse representation framework, which fuses the double tight frame (DTF) into the mixed-norm regularization for MR image reconstruction from undersampled k -space data. In this framework, MR image is decomposed into smooth and nonsmooth regions. For the smooth regions, the wavelet TF-based weighted L 1 -norm regularization is developed to reconstruct piecewise-smooth information of image. For nonsmooth regions, we introduce the curvelet TF-based robust L 1 , a -norm regularization with the parameter to preserve the edge structural details and texture. To estimate the reasonable parameter, an adaptive parameter selection scheme is designed in robust L 1 , a -norm regularization. Experimental results demonstrate that the proposed method can achieve the best image reconstruction results when compared with other existing methods in terms of quantitative metrics and visual effect