M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In Learning in Graphical Models. Kluwer AP, 1998. Home/Search Document Details and Download
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Scalable, Balanced Model-based Clustering - Zhong, Ghosh (Correct)
....to soft EM clustering, where each sample o gets fractionally assigned to a cluster j according to the posterior probability P (j o, #) and each model is trained using the posterior probability weighted samples. An information theoretic analysis of these two assignment strategies has been given in [21]. Let us analyze these two algorithms from the perspective of objective function and explain why they usually perform very similarly in practice. The general log likelihood objective function to be maximized for model based partitional clustering can be written as # , 2.1) where # ....
M. Kearns, Y. Mansour, and A. Y. Ng. An informationtheoretic analysis of hard and soft assignment methods for clustering. In Proc. 13th Uncertainty in Artificial Intelligence, pages 282--293, 1997.
An Algorithm for Non-distance Based Clustering in High.. - Zhu, Li (2002) (Correct)
....small number of prominent classes in a data set with multidimensionality. However, the centroids with huge dimensionality are hard to interpret in SOM. Moreover the robustness of SOM is also a problem especially when the number of clusters is unknown or the data set is sparse. 15 Kearns et al.[Kearns et al. 1998] gave an information theoretic analysis of hard assignments (used by K means) and soft assignments (used by EM algorithm) for clustering and proposed a posterior partition algorithm which is close to the soft assignments of EM for clustering. The relationship between maximum likelihood and ....
M. Kearns, Y. Mansour, and A. Y. Ng, \An Information-theoretic Analysis of Hard and Soft Assignment Methods for Clustering," In Learning in Graphical Models. Kluwer AP, 1998.
Approximation Algorithms for NP-Hard Clustering Problems - Mettu (2002) (Correct)
.... and astrophysics (see, e.g. 8, 32] for pointers to the literature) A classic approach to the problem of learning mixtures of Gaussians is to utilize a local search technique called Expectation Maximization (EM) The EM algorithm is often described as a probabilistic version of k means [24]. The objective of the EM algorithm is to maximize the likelihood between the actual and computed parameters of the Gaus11 sians. Just as k means is susceptible to local minima, unless the initial parameters are chosen carefully, the EM algorithm is susceptible to local maxima. The first ....
M. Kearns, Y. Mansour, and A. Ng. An information-theoretic analysis of hard and soft assignment methods of clustering. In Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, pages 282--293, August 1997. 110
Discovering Clusters in Motion Time-Series Data - Alon, Sclaroff, Kollios (2003) (5 citations) (Correct)
....given in this paper enables soft assignment, whereby each sequence is partially assigned to a cluster according to the cluster a posteriori probability given the sequence. This EM based algorithm is much preferred in noisy data situations or when the underlying densities are with more overlap [10]. 3. Background: HMMs In this section, we give a brief review of hidden Markov models (HMMs) Our main purpose is to define notation used in our clustering formulation. For a detailed overview of HMMs, readers are directed to [14] A complete specification of a first order HMM with a simple ....
M. Kearns, Y. Mansour, and A. Ng. An informationtheoretic analysis of hard and soft assignment methods for clustering. In Uncertainty in Artificial Intelligence, pages 282--293, 1997.
Algorithms for Clustering High Dimensional and - Tao (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In Learning in Graphical Models. Kluwer AP, 1998.
An Algorithm for Non-distance Based Clustering in High.. - Zhu, Li (2002) (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng, \An Information-theoretic Analysis of Hard and Soft Assignment Methods for Clustering," In Learning in Graphical Models. Kluwer AP, 1998.
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Kearns, M., Y. Mansour, and A. Y. Ng (1997). An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-- 97), San Francisco, CA, pp. 282--293. Morgan Kaufmann Publishers.
An MDL Framework for Data Clustering - Kontkanen, Al. (2004) (1 citation) (Correct)
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Kearns, M., Y. Mansour, and A. Y. Ng (1997). An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-- 97), San Francisco, CA, pp. 282--293. Morgan Kaufmann Publishers.
Soft-LOST: EM on a Mixture of Oriented Lines - O'Grady, Pearlmutter (2004) (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pages 282--293, 1997.
Clustering with Bregman Divergences - Arindam Banerjee Srujana (2004) (2 citations) (Correct)
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M. Kearns, Y. Mansour, and A. Ng. An informationtheoretic analysis of hard and soft assignment methods for clustering. In Proc. 13th UAI, pages 282-293, 1997.
Generative Model-based Clustering of Directional Data - Banerjee, Dhillon, Ghosh, Sra (2003) (1 citation) (Correct)
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M. Kearns, Y. Mansour, and A. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In 13th Annual Conf. on Uncertainty in Arti cial Intelligence (UAI97), 1997.
Learning Structure and Concepts in Data Through Data Clustering - Hamerly (2003) (Correct)
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Michael Kearns, Yishay Mansour, and Andrew Y. Ng. An informationtheoretic analysis of hard and soft assignment methods for clustering. In Proceedings of Uncertainty in Arti cial Intelligence, pages 282-293. AAAI, 1997.
A Particle Visualization Framework for Clustering and Anomaly .. - Davidson, Ward (Correct)
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M. Kearns, Y. Mansour, A. Ng, "An information-theoretic analysis of hard and soft assignment methods for clustering," Proc. 13th Conference on Uncertainty in Artificial Intelligence, 1997.
Alternatives to the k-Means Algorithm That Find Better.. - Hamerly, Elkan (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of Uncertainty in Arti cial Intelligence, pages 282-293. AAAI, 1997.
Clustering with Bregman Divergences - Banerjee, Merugu, Dhillon, Ghosh (2004) (2 citations) (Correct)
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M. Kearns, Y. Mansour, and A. Ng. An informationtheoretic analysis of hard and soft assignment methods for clustering. In Proc. 13th UAI, pages 282-293, 1997.
Self-Organizing Mixture Models - Verbeek, Vlassis, Kröse (2004) (Correct)
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M. J. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In M.I. Jordan, editor, Learning in Graphical Models, pages 495--520. Kluwer, 1998.
Active Semi-Supervision for Pairwise Constrained Clustering - Basu, Banjeree, Mooney (2004) (4 citations) (Correct)
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Michael Kearns, Yishay Mansour, and Andrew Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In Proc. of 13th Conf. on Uncertainty in Artificial Intelligence (UAI-97), pages 282--293, 1997.
Formant Re-Synthesis Of Dysarthric Speech - Alexander Kain Xiaochuan (Correct)
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Michael Kearns, Yishay Mansour, and Andrew Y Ng, "An information-theoretic analysis of hard and soft assignment methods for clustering," in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence. 1997, pp. 282--293, Morgan Kaufmann.
A Probabilistic Framework for Semi-Supervised Clustering - Basu, Bilenko, Mooney (2004) (3 citations) (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of 13th Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 282--293, 1997.
Appendix A - Experimental Details This (Correct)
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Kearns, M., Mansour, Y., and Ng, A. Y. An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of Uncertainty in Arti cial Intelligence (1997), pp. 282-293.
Hard-LOST: Modified k-Means for Oriented Lines - O'Grady, Pearlmutter (2004) (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pages 282-- 293, 1997.
Soft-LOST: EM on a Mixture of Oriented Lines - O'Grady, Pearlmutter (2004) (Correct)
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M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pages 282--293, 1997.
Expectation Maximization for Clustering on Hyperspheres - Arindam Banerjee Inderjit (Correct)
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M. Kearns, Y. Mansour, and A. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In 13th Annual Conf. on Uncertainty in Artificial Intelligence (UAI97), 1997.
Generative Model-based Clustering of Directional Data - Banerjee, Dhillon, Ghosh, Sra (2003) (1 citation) (Correct)
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M. Kearns, Y. Mansour, and A. Ng. An information-theoretic analysis of hard and soft assignment methods for clustering. In 13th Annual Conf. on Uncertainty in Artificial Intelligence (UAI97), 1997.
A Minimum Encoding Approach to Document Clustering - Palin, Pasanen, Roos (Correct)
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Kearns M., Mansour Y., and Ng A.Y. \An information-theoretic analysis of hard and soft assignment methods for clustering", in Proc. Uncertainty in Arti cial Intelligence, pp. 282{ 293, 1997.
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