Advances in Self Organising Maps

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari

Highlighting objects of interest in an image by integrating saliency and depth

Stereo images have been captured primarily for 3D reconstruction in the past. However, the depth information acquired from stereo can also be used along with saliency to highlight certain objects in a scene. This approach can be used to make still images more interesting to look at, and highlight objects of interest in the scene. We introduce this novel direction in this paper, and discuss the theoretical framework behind the approach. Even though we use depth from stereo in this work, our approach is applicable to depth data acquired from any sensor modality. Experimental results on both indoor and outdoor scenes demonstrate the benefits of our algorithm

Saliency Prediction for Mobile User Interfaces

We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.Comment: Paper accepted at WACV 201

Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions

Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (DPs), which leads to weak predictions. To address this issue, this paper introduces a hierarchical multi-relational graph representation learning (HMGRL) approach. Within the framework of HMGRL, we leverage a wealth of drug-related heterogeneous data sources to construct heterogeneous graphs, where nodes represent drugs and edges denote clear and various associations. The relational graph convolutional network (RGCN) is employed to capture diverse explicit relationships between drugs from these heterogeneous graphs. Additionally, a multi-view differentiable spectral clustering (MVDSC) module is developed to capture multiple valuable implicit correlations between DPs. Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations. Subsequently, multiple DP representations are generated through graph cutting, each emphasizing distinct implicit correlations. The graph-cutting strategy enables our HMGRL to identify strongly connected communities of graphs, thereby reducing the fusion of irrelevant features. By combining every representation view of a DP, we create high-level DP representations for predicting DDIs. Two genuine datasets spanning three distinct tasks are adopted to gauge the efficacy of our HMGRL. Experimental outcomes unequivocally indicate that HMGRL surpasses several leading-edge methods in performance.Comment: 14 pages,10 figure

Insect neuroethology of reinforcement learning

Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning

2006 Special Issue www.elsevier.com/locate/neunet Graph-based normalization and whitening for non-linear data analysis

In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for “global normalization”; we then adapt these measures using a weighted graph to build a local normalization called “graph-based ” normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the “global ” and the “local ” problem. c ○ 2006 Elsevier Ltd. All rights reserved

Graph-based normalization for non-linear data analysis (I)

International audienceIn this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get, in average, spherical neighbourhood with unit radius. In a first paragraph we show why this algorithm can be useful as a preliminary for some neural algorithms as those that need to compute geodesic distance. Then, we present the algorithm, its stochastic version and some graphical results. Finally, we observe the effects of the algorithm on the reconstruction of geodesic distance by running Dijksrta's algorithm and on the performance of Kohonen maps

Neural Networks 2006 Special Issue "Advances in Self-Organizing Maps-WSOM 05"

Special issue of Neural Networks Journal after the WSOM 05 ConferenceSpecial issue of Neural Networks Journal after the WSOM 05 ConferenceNeural Networks Volume 19, Issues 6-7, Pages 721-976 (July-August 2006) Advances in Self Organising Maps - WSOM'05 Edited by Marie Cottrell and Michel Verleysen 1. Advances in Self-Organizing Maps Pages 721-722 Marie Cottrell and Michel Verleysen 2. Self-organizing neural projections Pages 723-733 Teuvo Kohonen 3. Homeostatic synaptic scaling in self-organizing maps Pages 734-743 Thomas J. Sullivan and Virginia R. de Sa 4. Topographic map formation of factorized Edgeworth-expanded kernels Pages 744-750 Marc M. Van Hulle 5. Large-scale data exploration with the hierarchically growing hyperbolic SOM Pages 751-761 Jörg Ontrup and Helge Ritter 6. Batch and median neural gas Pages 762-771 Marie Cottrell, Barbara Hammer, Alexander Hasenfuß and Thomas Villmann 7. Fuzzy classification by fuzzy labeled neural gas Pages 772-779 Th. Villmann, B. Hammer, F. Schleif, T. Geweniger and W. Herrmann 8. On the equivalence between kernel self-organising maps and self-organising mixture density networks Pages 780-784 Hujun Yin 9. Adaptive filtering with the self-organizing map: A performance comparison Pages 785-798 Guilherme A. Barreto and Luís Gustavo M. Souza 10. The Self-Organizing Relationship (SOR) network employing fuzzy inference based heuristic evaluation Pages 799-811 Takanori Koga, Keiichi Horio and Takeshi Yamakawa 11. SOM's mathematics Pages 812-816 J.C. Fort 12. Performance analysis of LVQ algorithms: A statistical physics approach Pages 817-829 Anarta Ghosh, Michael Biehl and Barbara Hammer 13. Self-organizing map algorithm and distortion measure Pages 830-837 Joseph Rynkiewicz 14. Understanding and reducing variability of SOM neighbourhood structure Pages 838-846 Patrick Rousset, Christiane Guinot and Bertrand Maillet 15. Assessing self organizing maps via contiguity analysis Pages 847-854 Ludovic Lebart 16. Fast algorithm and implementation of dissimilarity self-organizing maps Pages 855-863 Brieuc Conan-Guez, Fabrice Rossi and Aïcha El Golli 17. Graph-based normalization and whitening for non-linear data analysis Pages 864-876 Catherine Aaron 18. Unfolding preprocessing for meaningful time series clustering Pages 877-888 Geoffroy Simon, John A. Lee and Michel Verleysen 19. Local multidimensional scaling Pages 889-899 Jarkko Venna and Samuel Kaski 20. Spherical self-organizing map using efficient indexed geodesic data structure Pages 900-910 Yingxin Wu and Masahiro Takatsuka 21. Advanced visualization of Self-Organizing Maps with vector fields Pages 911-922 Georg Pölzlbauer, Michael Dittenbach and Andreas Rauber 22. Online data visualization using the neural gas network Pages 923-934 Pablo A. Estévez and Cristián J. Figueroa 23. TreeSOM: Cluster analysis in the self-organizing map Pages 935-949 Elena V. Samsonova, Joost N. Kok and Ad P. IJzerman 24. Self-organizing neural networks to support the discovery of DNA-binding motifs Pages 950-962 Shaun Mahony, Panayiotis V. Benos, Terry J. Smith and Aaron Golden 25. A descriptive method to evaluate the number of regimes in a switching autoregressive model Pages 963-972 Madalina Oltean