The growing hierarchical self-organizing map

By Michael Dittenbach, Dieter Merkl, Andreas Rauber. 2000

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Francesco Locatello
2. Gunnar Rätsch
3. J. Urgen Schmidhuber
4. Bernhard Schölkopf
5. Yoshua Bengio
6. Vincent Fortuin
7. Heiko Strathmann
8. Matthias Hüser

In Computer Vision and Pattern Recognition

1. Francesco Locatello
2. Gunnar Rätsch
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Vincent Fortuin
6. Heiko Strathmann
7. Matthias Hüser
8. Tapani Raiko

In Signal Processing

1. Francesco Locatello
2. Gunnar Rätsch
3. Vincent Fortuin
4. Heiko Strathmann
5. Matthias Hüser
6. Timo Honkela
7. J. Urgen Schmidhuber
8. Tapani Raiko

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “s had a single layer. Methods for deeper UL FNNs include hierarchical (Sec. 4.3) self-organizing Kohonen maps (e.g., Koikkalainen and Oja, 1990; Lampinen and Oja, 1992; Versino and Gambardella, 1996; Dittenbach et al., 2000; Rauber et al., 2002), hierarchical Gaussian potential function networks (Le”
• Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018, methodology): “There are approaches to turn the SOM into a “deeper” model [8], combine it with multi-layer perceptrons [11] or with metric learning [29].”
• Self-organization and missing values in SOM and GTM (2015, background): “Previously many solutions, such as hierarchical [15] and growing maps [16,17], have been proposed to tackle this issue.”
• SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019, methodology): “There are approaches to turn the SOM into a “deeper” model (Dittenbach et al., 2000), combine it with multi-layer perceptrons (Furukawa et al.”
• Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, unknown): “1726] counter-propagation [491, 1206, 1305] cross-validation [69, 173, 223, 1684, 1811] cursive character [167, 610, 1227] curvilinear component analysis (CCA) [530, 1013, 1875] data fusion [159, 589, 1017, 1061, 1545, 1603, 1746, 1750, 1917, 2065, 2066, 2068, 2069] data mining [17, 16, 18, 215, 304”
• Content-based organization and visualization of music archives (2002, background): “A particularly interesting variant regarding the organization of large music archives is the adaptive GHSOM [6] which provides a hierarchical organization and representation of the data.”