Unsupervised Classification of PolSAR Imagery via Kernel Sparse Subspace Clustering

Song, Hui; Yang, Wen; Zhong, Neng; Xu, Xin

doi:10.1109/lgrs.2016.2593098

Cited by 14 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from this, we also compute the time complexity of both algorithms over similar simulation environment. The algorithm processing time of proposed RMSC is 85% faster as compared to work carried out by Song et al [32]. Hence, proposed system offer cost effective subspace clustering technique for high dimensional data.…”

Section: Results Analysismentioning

confidence: 84%

“…For effective analysis, the proposed system RMSC is compared with work done by Song et al [32] towards sparse subspace clustering. The work carried out by Song et al [32] have used Hermitian positive definite embedded over Hilbert space in order to develop a sparse subspace clustering. We do fine tune the work of Song et al by implementing the same dataset of Yale on our performance parameter of accuracy and error.…”

Section: Results Analysismentioning

confidence: 99%

“…Most importantly, the level of noise doesn't really effects the accuracy level of proposed RMSC. The main reason for this outcome is that Song et al [32] have used an integrated technique of classification as well as weighted joint coding in order to obtain better contextual data from subspaces. However, it leads to minimization of iterative speed for which reason the search space cannot be much optimized over the progressive subspaces.…”

Section: Results Analysismentioning

confidence: 99%

See 2 more Smart Citations

RMSC: Robust modeling of subspace clustering for high dimensional data

Radhika

et al. 2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

Abstract-Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data.

show abstract

Section: Results Analysismentioning

confidence: 84%

Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%

See 1 more Smart Citation

RMSC: Robust modeling of subspace clustering for high dimensional data

Radhika

et al. 2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

show abstract

“…3 highlight the performance of clustering accuracy concerning occlusion. Although the trend of clustering accuracy diminishes with increase of subjects during the analysis, the outcome shows that proposed system offers comparatively more clustering accuracy as compared to the approach of Song et al [31]. An interesting observation, in this case, is that irrespective of any proportion of occlusion level proposed system offers better sustainability against the noise level too.…”

Section: Performancementioning

confidence: 74%

“…The performance parameter considered to assess the effectiveness of the proposed system is clustering accuracy and error rate, where the results are captured for different states of occlusion. The study outcome of the proposed system is compared with the most related work presented by Song et al [31] who have performed Sparse Subspace Clustering (SSC) using Hilbert space. The study outcome of proposed system has been also compared with out prior model RMSC [32].…”

Section: Performancementioning

confidence: 99%

Framework for novel subspace clustering using search optimization methodology

Radhika¹,

N²,

J³

et al. 2018

IJET

View full text Add to dashboard Cite

Improving the yield as well as the perform of subspace clustering is one of the less-investigated topics in high-dimensional data. After reviewing existing approaches, it seriously felt that there is a need for classification of data points retrieved from a different number of subspace. The proposed study has presented a novel framework that targets to improve the accuracy of subspace clustering by addressing the problem associated with the exist of occlusion noise and dimensional complexity. An analytical approach as been proposed to design this framework with more emphasis on outlier minimization followed by obtaining optimal clusters. The technique also introduces a simple search optimization method, which is less iterative and is more productive for identifying the élite outcomes in each iterative step. The study outcome shows superior accuracy with a low rate of error when compared with the conventional approach.

show abstract