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Scalable Spectral Clustering with Weighted PageRank
"... Abstract. In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral ..."
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Abstract. In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral
Large Scale Spectral Clustering with Landmark-Based Representation
- PROCEEDINGS OF THE TWENTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2011
"... Spectral clustering is one of the most popular clustering approaches. Despite its good performance, it is limited in its applicability to large-scale problems due to its high computational complexity. Recently, many approaches have been proposed to accelerate the spectral clustering. Unfortunately, ..."
Abstract
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Cited by 23 (1 self)
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, these methods usually sacrifice quite a lot information of the original data, thus result in a degradation of performance. In this paper, we propose a novel approach, called Landmark-based Spectral Clustering (LSC), for large scale clustering problems. Specifically, we select p ( ≪ n) representative data points
Feature Selection in Spectral Clustering
"... Spectral clustering is a powerful technique in clustering specially when the structure of data is not linear and classical clustering methods lead to fail. In this paper, we propose a spectral clustering algorithm with a feature selection schema based on extracted features of Kernel PCA. In the prop ..."
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Spectral clustering is a powerful technique in clustering specially when the structure of data is not linear and classical clustering methods lead to fail. In this paper, we propose a spectral clustering algorithm with a feature selection schema based on extracted features of Kernel PCA
Spectral Clustering Based on Local PCA
"... We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods, and then applies s ..."
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We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods, and then applies
Multiway spectral clustering with out-ofsample extensions through weighted kernel PCA
- IEEE Trans. Pattern Anal. Mach. Intell
, 2010
"... Abstract—A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual least-squares support vector machine (LS-SVM) formulations. The formulation allows the extension to out-of-sample poi ..."
Abstract
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Cited by 18 (6 self)
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Abstract—A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual least-squares support vector machine (LS-SVM) formulations. The formulation allows the extension to out
Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering
- SIGIR'02
, 2002
"... A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text dcuments as weighted und rected and weighted bipartite graphs. Spectral graph clustering algorithms are used for partitioning sentences of the documents into topical groups with sentence ..."
Abstract
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Cited by 64 (6 self)
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link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion
A COMBINED METHOD FOR DETECTING SPAM MACHINES ON A TARGET NETWORK
"... The HITS and PageRank algorithms and K-Means clustering algorithm are two main methods for detecting spam machines. In PageRank algorithm, it is proposed to calculate weights based on different factors. Correct selection of weights has important role in the accuracy of the algorithm. In this paper, ..."
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The HITS and PageRank algorithms and K-Means clustering algorithm are two main methods for detecting spam machines. In PageRank algorithm, it is proposed to calculate weights based on different factors. Correct selection of weights has important role in the accuracy of the algorithm. In this paper
RESEARCH Open Access Peptide identification based on fuzzy classification and clustering
"... Background: The sequence database searching has been the dominant method for peptide identification, in which a large number of peptide spectra generated from LC/MS/MS experiments are searched using a search engine against theoretical fragmentation spectra derived from a protein sequences database o ..."
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or a spectral library. Selecting trustworthy peptide spectrum matches (PSMs) remains a challenge. Results: A novel scoring method named FC-Ranker is developed to assign a nonnegative weight to each target PSM based on the possibility of its being correct. Particularly, the scores of PSMs are updated
unknown title
"... Abstract Spectral clustering refers to a class of techniques which rely on the eigen-structure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in dif-ferent clusters having low similarity. In this paper, we derive a ..."
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new cost function for spectral clustering based on a measure of error between agiven partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing this cost function with respect tothe partition leads to a new spectral clustering algorithm. Minimizing with respect
Results 1 - 10
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55
