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144
Spectral Clustering and Embedding with Hidden Markov Models
"... Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering of vector inputs is straightforward, extensions to structured data or time-series data remain less explored. This paper ..."
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Cited by 20 (1 self)
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shows that using probabilistic pairwise kernel estimates between parametric models provides improved experimental results for unsupervised clustering and visualization of real and synthetic datasets. Results are compared with a fully parametric baseline method (a mixture of hidden Markov models) and a
Sparse Manifold Clustering and Embedding
"... We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds. Similar to most dimensionality reduction methods, SMCE finds a small neighborhood around each data point and connects ..."
Abstract
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Cited by 31 (1 self)
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-dimensional affine subspace. The optimal solution encodes information that can be used for clustering and dimensionality reduction using spectral clustering and embedding. Moreover, the size of the optimal neighborhood of a data point, which can be different for different points, provides an estimate
Membership embedding space approach and spectral clustering
- In KES
, 2007
"... Abstract. The data representation strategy termed “Membership Embedding” is a type of similarity-based representation that uses a set of data items in an input space as reference points (probes), and represents all data in terms of their membership to the fuzzy concepts represented by the probes. Th ..."
Abstract
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Cited by 1 (0 self)
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. The technique has been proposed as a concise representation for improving the data clustering task. In this contribution, it is shown that this representation strategy yields a spectral clustering formulation, and this may account for the improvement in clustering performance previously reported
Dimensionality Reduction for Spectral Clustering
"... Spectral clustering is a flexible clustering methodology that is applicable to a variety of data types and has the particular virtue that it makes few assumptions on cluster shapes. It has become popular in a variety of application areas, particularly in computational vision and bioinformatics. The ..."
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Cited by 2 (1 self)
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operator is incorporated in the relaxed optimization functional. We optimize this functional over both the projection and the spectral embedding. Experiments on simulated and real data show that this approach yields significant improvements in the performance of spectral clustering. 1
Fuzzy Relational Spectral Clustering Method for Document Clustering
"... Abstract Correlation Preserving Indexing is a spectral clustering method which discovers intrinsic structures embedded in high-dimensional document space. But the problem is to predict the result of one variable based on another variable is not suitable for all the situations. So, the directed Ridge ..."
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Abstract Correlation Preserving Indexing is a spectral clustering method which discovers intrinsic structures embedded in high-dimensional document space. But the problem is to predict the result of one variable based on another variable is not suitable for all the situations. So, the directed
Greedy spectral embedding
"... Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n × n matrix where n is the number of examples. Following up on previously proposed techniques to speed-up kernel methods by focusing on a subset of m examples, we study ..."
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Cited by 20 (2 self)
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Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n × n matrix where n is the number of examples. Following up on previously proposed techniques to speed-up kernel methods by focusing on a subset of m examples, we study
Learning Deep Representations for Graph Clustering
"... Recently deep learning has been successfully adopted in many applications such as speech recognition and im-age classification. In this work, we explore the possi-bility of employing deep learning in graph clustering. We propose a simple method, which first learns a non-linear embedding of the origi ..."
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Recently deep learning has been successfully adopted in many applications such as speech recognition and im-age classification. In this work, we explore the possi-bility of employing deep learning in graph clustering. We propose a simple method, which first learns a non-linear embedding
Clustered Embedding of Massive Social Networks
"... Abstract The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in the network. Despite much research on proximity measures, th ..."
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Cited by 5 (4 self)
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, there is a lack of techniques to ef�ciently and accurately compute proximity measures for largescale social networks. In this paper, we embed the original massive social graph into a much smaller graph, using a novel dimensionality reduction technique termed Clustered Spectral Graph Embedding
Clustered Embedding of Massive Social Networks
"... Abstract — The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in the network. Despite much research on proximity measures, ..."
Abstract
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, there is a lack of techniques to efficiently and accurately compute proximity measures for large-scale social networks. In this paper, we embed the original massive social graph into a much smaller graph, using a novel dimension-ality reduction technique termed Clustered Spectral Graph Embed-ding. We show
Results 1 - 10
of
144
