References of "Pattern Recognition"
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See detailGaussian fields for semi-supervised regression and correspondence learning
Verbeek, Jakob J.; Vlassis, Nikos UL

in Pattern Recognition (2006), 39(10), 1864-1875

Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be used for semi-supervised ... [more ▼]

Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be used for semi-supervised regression on high-dimensional data. We propose an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Furthermore, we show how a recent generalization of the LLE algorithm for correspondence learning can be cast into the GF framework, which obviates the need to choose a representation dimensionality. [less ▲]

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See detailThe global k-means clustering algorithm
Likas, Aristidis; Vlassis, Nikos UL; Verbeek, Jakob J.

in Pattern Recognition (2003), 36(2), 451-461

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N ... [more ▼]

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts. [less ▲]

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