The Experts below are selected from a list of 4527 Experts worldwide ranked by ideXlab platform
Stan Szpakowicz - One of the best experts on this subject based on the ideXlab platform.
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hierarchical topical segmentation with Affinity Propagation
International Conference on Computational Linguistics, 2014Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a document, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes at lower levels correspond to finer topical fluctuations. For each segment in the tree, HAPS identifies a segment centre ‐ a sentence or a paragraph which best describes its contents. We evaluate the segmenter on a subset of a novel manually segmented by several annotators, and on a dataset of Wikipedia articles. The results suggest that hierarchical segmentations produced by HAPS are better than those obtained by iteratively running several one-level segmenters. An additional advantage of HAPS is that it does not require the “gold standard” number of segments in advance.
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COLING - Hierarchical Topical Segmentation with Affinity Propagation
2014Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a document, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes at lower levels correspond to finer topical fluctuations. For each segment in the tree, HAPS identifies a segment centre ‐ a sentence or a paragraph which best describes its contents. We evaluate the segmenter on a subset of a novel manually segmented by several annotators, and on a dataset of Wikipedia articles. The results suggest that hierarchical segmentations produced by HAPS are better than those obtained by iteratively running several one-level segmenters. An additional advantage of HAPS is that it does not require the “gold standard” number of segments in advance.
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linear text segmentation using Affinity Propagation
Empirical Methods in Natural Language Processing, 2011Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres -- data points which best describe all other data points within the segment. APS iteratively passes messages in a cyclic factor graph, until convergence. Each iteration works with information on all available similarities, resulting in high-quality results. APS scales linearly for realistic segmentation tasks. We derive the algorithm from the original Affinity Propagation formulation, and evaluate its performance on topical text segmentation in comparison with two state-of-the art segmenters. The results suggest that APS performs on par with or outperforms these two very competitive baselines.
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EMNLP - Linear Text Segmentation Using Affinity Propagation
2011Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres -- data points which best describe all other data points within the segment. APS iteratively passes messages in a cyclic factor graph, until convergence. Each iteration works with information on all available similarities, resulting in high-quality results. APS scales linearly for realistic segmentation tasks. We derive the algorithm from the original Affinity Propagation formulation, and evaluate its performance on topical text segmentation in comparison with two state-of-the art segmenters. The results suggest that APS performs on par with or outperforms these two very competitive baselines.
Anna Kazantseva - One of the best experts on this subject based on the ideXlab platform.
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hierarchical topical segmentation with Affinity Propagation
International Conference on Computational Linguistics, 2014Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a document, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes at lower levels correspond to finer topical fluctuations. For each segment in the tree, HAPS identifies a segment centre ‐ a sentence or a paragraph which best describes its contents. We evaluate the segmenter on a subset of a novel manually segmented by several annotators, and on a dataset of Wikipedia articles. The results suggest that hierarchical segmentations produced by HAPS are better than those obtained by iteratively running several one-level segmenters. An additional advantage of HAPS is that it does not require the “gold standard” number of segments in advance.
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COLING - Hierarchical Topical Segmentation with Affinity Propagation
2014Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a document, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes at lower levels correspond to finer topical fluctuations. For each segment in the tree, HAPS identifies a segment centre ‐ a sentence or a paragraph which best describes its contents. We evaluate the segmenter on a subset of a novel manually segmented by several annotators, and on a dataset of Wikipedia articles. The results suggest that hierarchical segmentations produced by HAPS are better than those obtained by iteratively running several one-level segmenters. An additional advantage of HAPS is that it does not require the “gold standard” number of segments in advance.
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linear text segmentation using Affinity Propagation
Empirical Methods in Natural Language Processing, 2011Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres -- data points which best describe all other data points within the segment. APS iteratively passes messages in a cyclic factor graph, until convergence. Each iteration works with information on all available similarities, resulting in high-quality results. APS scales linearly for realistic segmentation tasks. We derive the algorithm from the original Affinity Propagation formulation, and evaluate its performance on topical text segmentation in comparison with two state-of-the art segmenters. The results suggest that APS performs on par with or outperforms these two very competitive baselines.
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EMNLP - Linear Text Segmentation Using Affinity Propagation
2011Co-Authors: Anna Kazantseva, Stan SzpakowiczAbstract:This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres -- data points which best describe all other data points within the segment. APS iteratively passes messages in a cyclic factor graph, until convergence. Each iteration works with information on all available similarities, resulting in high-quality results. APS scales linearly for realistic segmentation tasks. We derive the algorithm from the original Affinity Propagation formulation, and evaluate its performance on topical text segmentation in comparison with two state-of-the art segmenters. The results suggest that APS performs on par with or outperforms these two very competitive baselines.
Long Quan - One of the best experts on this subject based on the ideXlab platform.
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Joint Affinity Propagation for multiple view segmentation
Proceedings of the IEEE International Conference on Computer Vision, 2007Co-Authors: Jianxiong Xiao, Ping Tan, Jie Wang, Long QuanAbstract:A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse Affinity Propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised Affinity Propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
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ICCV - Joint Affinity Propagation for Multiple View Segmentation
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Jianxiong Xiao, Ping Tan, Jingdong Wang, Long QuanAbstract:A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse Affinity Propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised Affinity Propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
Jianxiong Xiao - One of the best experts on this subject based on the ideXlab platform.
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Joint Affinity Propagation for multiple view segmentation
Proceedings of the IEEE International Conference on Computer Vision, 2007Co-Authors: Jianxiong Xiao, Ping Tan, Jie Wang, Long QuanAbstract:A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse Affinity Propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised Affinity Propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
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ICCV - Joint Affinity Propagation for Multiple View Segmentation
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Jianxiong Xiao, Ping Tan, Jingdong Wang, Long QuanAbstract:A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse Affinity Propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised Affinity Propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
Brendan J Frey - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Affinity Propagation
arXiv: Learning, 2012Co-Authors: Inmar E. Givoni, Clement Chung, Brendan J FreyAbstract:Affinity Propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend Affinity Propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation. We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographical location and strain subtypes. Finally we report results on using the method for the analysis of mass spectra, showing it performs favorably compared to state-of-the-art methods.
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UAI - Hierarchical Affinity Propagation
2011Co-Authors: Inmar E. Givoni, Clement Chung, Brendan J FreyAbstract:Affinity Propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend Affinity Propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation. We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographical location and strain subtypes. Finally we report results on using the method for the analysis of mass spectra, showing it performs favorably compared to state-of-the-art methods.
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non metric Affinity Propagation for unsupervised image categorization
International Conference on Computer Vision, 2007Co-Authors: Delbert Dueck, Brendan J FreyAbstract:Unsupervised categorization of images or image parts is often needed for image and video summarization or as a preprocessing step in supervised methods for classification, tracking and segmentation. While many metric-based techniques have been applied to this problem in the vision community, often, the most natural measures of similarity (e.g., number of matching SIFT features) between pairs of images or image parts is non-metric. Unsupervised categorization by identifying a subset of representative exemplars can be efficiently performed with the recently-proposed 'Affinity Propagation' algorithm. In contrast to k-centers clustering, which iteratively refines an initial randomly-chosen set of exemplars, Affinity Propagation simultaneously considers all data points as potential exemplars and iteratively exchanges messages between data points until a good solution emerges. When applied to the Olivetti face data set using a translation-invariant non-metric similarity, Affinity Propagation achieves a much lower reconstruction error and nearly halves the classification error rate, compared to state-of-the-art techniques. For the more challenging problem of unsupervised categorization of images from the CaltechlOl data set, we derived non-metric similarities between pairs of images by matching SIFT features. Affinity Propagation successfully identifies meaningful categories, which provide a natural summarization of the training images and can be used to classify new input images.
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ICCV - Non-metric Affinity Propagation for unsupervised image categorization
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Delbert Dueck, Brendan J FreyAbstract:Unsupervised categorization of images or image parts is often needed for image and video summarization or as a preprocessing step in supervised methods for classification, tracking and segmentation. While many metric-based techniques have been applied to this problem in the vision community, often, the most natural measures of similarity (e.g., number of matching SIFT features) between pairs of images or image parts is non-metric. Unsupervised categorization by identifying a subset of representative exemplars can be efficiently performed with the recently-proposed 'Affinity Propagation' algorithm. In contrast to k-centers clustering, which iteratively refines an initial randomly-chosen set of exemplars, Affinity Propagation simultaneously considers all data points as potential exemplars and iteratively exchanges messages between data points until a good solution emerges. When applied to the Olivetti face data set using a translation-invariant non-metric similarity, Affinity Propagation achieves a much lower reconstruction error and nearly halves the classification error rate, compared to state-of-the-art techniques. For the more challenging problem of unsupervised categorization of images from the CaltechlOl data set, we derived non-metric similarities between pairs of images by matching SIFT features. Affinity Propagation successfully identifies meaningful categories, which provide a natural summarization of the training images and can be used to classify new input images.
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ICMLA - Learning in Biomedicine and Bioinformatics Using Affinity Propagation
Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 2007Co-Authors: Brendan J FreyAbstract:Data sets arising in biomedicine and bioinformatics are often huge and consist of quite different types of data (eg, sequence data and microarray measurements). Consequently, standard machine learning techniques usually cannot be directly applied. In this talk, I will describe an algorithm called Affinity Propagation and discuss why it offers flexibility in analyzing the kinds of data sets arising in bioinformatics and biomedicine. I'll describe applications in the areas of whole-genome transcript detection using microarrays, image segmentation, text analysis and motif discovery. Affinity Propagation can implemented in a couple dozen lines of MATLAB or C and is suitable for distributed computing environments, making it attractive for high-throughput computations.
