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Advanced Functional Materials, EarlyView.A nanounit‐assembled hydrogel employing a “pull‐and‐push” strategy simultaneously scavenges pro‐inflammatory cell‐free DNA (cfDNA) and delivers regenerative therapeutics in response to burn‐induced hyperthermia. By repolarizing macrophages and promoting angiogenesis, this multifunctional platform accelerates burn wound healing, offering a blueprint for
Han‐Sem Kim +9 more
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Spectral sparsification in spectral clustering
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Graph spectral clustering algorithms have been shown to be effective in finding clusters and generally outperform traditional clustering algorithms, such as k-means. However, they have scalibility issues in both memory usage and computational time. To overcome these limitations, the common approaches sparsify the similarity matrix by zeroing out some ...
Alireza Chakeri +2 more
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Spectral Cluster Maps Versus Spectral Clustering
2020The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods.
Slawomir T. Wierzchon +1 more
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Robust path-based spectral clustering
Pattern Recognition, 2008 Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data.
Hong Chang, Dit-Yan Yeung
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2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
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Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
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Spectral Clustering by Joint Spectral Embedding and Spectral Rotation [PDF]
IEEE Transactions on Cybernetics, 2020 Spectral clustering is an important clustering method widely used for pattern recognition and image segmentation. Classical spectral clustering algorithms consist of two separate stages: 1) solving a relaxed continuous optimization problem to obtain a real matrix followed by 2) applying K -means or spectral rotation to round the real matrix (i.e ...
Yanwei Pang, Jin Xie, Feiping Nie
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Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu 0001 +4 more
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Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu 0001 +4 more
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2003
In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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Compressed Spectral Clustering
2009 IEEE International Conference on Data Mining Workshops, 2009Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible.
Bin Zhao 0004, Changshui Zhang
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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
Recognizing and clustering similar faces are important for organizing digital photos. We present a novel clustering method based on spectral clustering to group faces in a photo album. The main contribution is the proposal of a distance metric that is robust to outlier features present in the facial images.
Biswaroop Palit +3 more
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Recognizing and clustering similar faces are important for organizing digital photos. We present a novel clustering method based on spectral clustering to group faces in a photo album. The main contribution is the proposal of a distance metric that is robust to outlier features present in the facial images.
Biswaroop Palit +3 more
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