Spectral Clustering by Subspace Randomization and Graph Fusion for High-Dimensional Data

Cai, Xiaosha; Huang, Dong; Wang, Chang‐Dong; Kwoh, Chee-Keong

doi:10.1007/978-3-030-47426-3_26

Cited by 18 publications

(16 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three conventional clustering methods are selected for comparison, including spectral clustering with normalized cut (Ncut), Kmeans and sparse subspace clustering (SSC) [30]. Two newer clustering algorithms from recent years are also added for comparison, USENC [31] and SC-SRGF [32]. As shown in the table, HFSC achieves the best performances among all datasets.…”

Section: Comprehensive Factorsmentioning

confidence: 99%

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

et al. 2022

View full text Add to dashboard Cite

Verification is one of the core steps in integrated circuits (ICs) manufacturing due to the multifarious defects and malicious hardware Trojans (HTs). In most cases, the effectiveness of the detection relies on the quality of the sample images of ICs. However, the high-precision and noiseless images are hard to capture due to the mechanical precision, manual error and environmental interference. In this paper, an effective approach for processing the low-quality image data of ICs is proposed. Our approach can successfully categorize the partial pictures of multiple objected ICs with low resolution and various noise. The proposed approach extracts the high-frequency texture components (HFTC) of the images and constructs a graph with the correlationship among features. Subsequently, the spectral clustering is conducted for obtaining the final cluster indicators. The low-quality images of ICs can be successfully categorized by the proposed approach, which will provide a data foundation for the following verification tasks. In order to evaluate the effectiveness of the proposed approach, several experiments are conducted in the simulated datasets, which are generated by corrupting the real-world data in different conditions. The clustering results reveal that our approach can achieve the best performance with good stability compared to the baselines.

show abstract

Section: Comprehensive Factorsmentioning

confidence: 99%

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

et al. 2022

View full text Add to dashboard Cite

show abstract

“…We collect all the spectral coordinates computed and add them as additional features to the boundary representation b i = (x i , y i , z i , r i ). We then use the high-dimensional data spectral clustering of [61] to cluster all object models within a category together. The algorithm of [61] provides a spectral clustering approach based on subspace randomization and graph fusion for high-dimensional data, which enables us to carry out both segmentation and co-segmentation tasks for a single shape or family of shape models (co-segmentation).…”

Section: Part Segmentationmentioning

confidence: 99%

“…We then use the high-dimensional data spectral clustering of [61] to cluster all object models within a category together. The algorithm of [61] provides a spectral clustering approach based on subspace randomization and graph fusion for high-dimensional data, which enables us to carry out both segmentation and co-segmentation tasks for a single shape or family of shape models (co-segmentation). To examine whether these added features to the original shape help with the segmentation task, we tested our off-the-shelf method on the Princeton Segmentation Benchmark [62].…”

Section: Part Segmentationmentioning

confidence: 99%

Medial Spectral Coordinates for 3D Shape Analysis

Rezanejad¹,

Khodadad²,

Pourreza³

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral coordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to isometric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.

show abstract

“…Yet a common limitation to most graph-based clustering methods is that they often rely on some predefined affinity graph, which lack the ability to learn the affinity graph adaptively. To deal with this limitation, some graph learning methods have been developed, aiming to learn a better graph via optimization or some heuristics [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Joint Multi-view Unsupervised Feature Selection and Graph Learning

Fang¹,

Dong²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Despite the recent progress, the existing multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selection, neglecting the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by either global structure learning or local structure learning, lacking the capability of graph learning with both global and local structural awareness. In light of this, this paper presents a joint multi-view unsupervised feature selection and graph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. Cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments demonstrate the superiority of our approach for both multi-view feature selection and graph learning tasks.

show abstract

Spectral Clustering by Subspace Randomization and Graph Fusion for High-Dimensional Data

Cited by 18 publications

References 24 publications

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

Medial Spectral Coordinates for 3D Shape Analysis

Joint Multi-view Unsupervised Feature Selection and Graph Learning

Contact Info

Product

Resources

About