2D diffuse optical imaging using clustered sparsity

Chen, Chen; Tian, Fenghua; Yao, Jixing; Liu, Hanli; Huang, Junzhou

doi:10.1109/isbi.2014.6867946

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If non-zero entries cluster in groups, 2,1 norm regularization can be used to exploit the group sparsity [11]. When the groups overlap, it comes to overlapping group sparsity [12], which could be used to solve clustered sparsity [13][8] [14] and tree sparsity [15] [16]. Overlapping group sparsity model is more generalized: if each entry is only in one group, it comes to the non-overlapping group sparsity; when each group contains only one entry, it is identical to standard sparsity with 1 norm regularization.…”

Section: Introductionmentioning

confidence: 99%

O(1) Algorithms for Overlapping Group Sparsity

Chen

Peng

Huang

2014

2014 22nd International Conference on Pattern Recognition

Self Cite

View full text Add to dashboard Cite

Sparsity based techniques have become very popular in machine learning, medical imaging and computer vision. Recently, with the emerging and development of structured sparsity, signals can be recovered more accurately. However, solving structured sparsity problems often involves much higher computational complexity. Few of existing works can reduce the computational complexity of such problems. Especially for overlapping group sparsity, the computational complexity for each entry is linear to the degree of overlapping, making it infeasible for large-scale problems. In this paper, we propose novel algorithms to efficiently address this issue, where the computational complexity for each entry is always O(1) and independent to the degree of overlapping. Experiments on 1D signal and 2D image demonstrate the effectiveness and efficiency of our methods. This work may inspire more scalable algorithms for structured sparsity.

show abstract