N. Slonim, N. Friedman, and N. Tishby. Agglomerative multivariate information bottleneck. In NIPS-14, 2001. Home/Search Document Details and Download
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Information-Theoretic Co-Clustering - Dhillon, Mallela, Modha (2003) (9 citations) (Correct)
....monotonically, converging to a local minimum. Recently, when considering a clustering framework using Bayesian belief networks, 10] proposed an iterative optimization method that amounts to a multivariate generalization of [20] and, once again, uses deterministic annealing. A later paper [18] presented a hard agglomerative algorithm for the same problem that has advantages over [10] in that it is simpler, fully deterministic, and non parametric. There is no need to identify cluster splits which is rather tricky . However, 18] pointed out that their agglomeration procedures do not ....
....once again, uses deterministic annealing. A later paper [18] presented a hard agglomerative algorithm for the same problem that has advantages over [10] in that it is simpler, fully deterministic, and non parametric. There is no need to identify cluster splits which is rather tricky . However, [18] pointed out that their agglomeration procedures do not scale linearly with the sample size as top down methods do : In this paper, we present a principled, top down hard clustering method that scales well. Also, the results in [18] amount to rst nding a word clustering followed by nding ....
[Article contains additional citation context not shown here]
N. Slonim, N. Friedman, and N. Tishby. Agglomerative multivariate information bottleneck. In NIPS-14, 2001.
Information-Theoretic Co-Clustering - Dhillon, Mallela, Modha (2003) (9 citations) (Correct)
....function, converging to a local minimum. Recently, when considering a general clustering framework using Bayesian belief networks, 10] proposed an iterative optimization method that amounts to a multivariate generalization of [20] and, once again, uses deterministic annealing. A later paper [17] presented an agglomerative algorithm for the same problem that has advantages over [10] in that it is simpler, fully deterministic, and non parametric. There is no need to identify cluster splits which is rather tricky. However, 17] pointed out that their agglomeration procedures do not scale ....
....and, once again, uses deterministic annealing. A later paper [17] presented an agglomerative algorithm for the same problem that has advantages over [10] in that it is simpler, fully deterministic, and non parametric. There is no need to identify cluster splits which is rather tricky. However, [17] pointed out that their agglomeration procedures do not scale linearly with the sample size as top down methods do : In this paper, we are concerned with a principled, top down method that scales well. Finally, the results in [17] amount to rst nding a word clustering followed by nding a ....
[Article contains additional citation context not shown here]
N. Slonim, N. Friedman, and N. Tishby. Agglomerative multivariate information bottleneck. In Advances in Neural Information Processing Systems (NIPS-14), 2001.
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