This paper deals with automatically learning the spatial distribution of a set of measurements: images, in the examples presented here. The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem of organizing an ensemble of images of an environment in terms of the positions from which the images were obtained...

A topographic map is a two-dimensional, nonlinear approximation of a potentially high-dimensional data manifold, which makes it an appealing instrument for visualizing and exploring high-dimensional data. The Self-Organizing Map (SOM) is the most widely used algorithm, and it has led to thousands of applications in very diverse areas. In this chapter, we will introduce the SOM algorithm, discus...

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensit...

This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this p...

Knowledge discovery in ubiquitous environments are usually conditioned by the data stream model, e.g., data is potentially infinite, arrives continuously and is subject to concept drift. These factors present additional challenges to standard data mining algorithms. Artificial Neural Networks (ANN) models are still poorly explored in these settings. State-of-the-art methods to deal with data st...

This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they be arranged to best explain their genesis? The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem where only limited prior odom...

To take further steps along the path toward true artificial intelligence, systems must be built that are capable of learning about the world around them through observation and explanation. These systems should be flexible and robust in the style of the human brain and little precompiled knowledge should be given initially. As a step toward achieving this lofty goal, this thesis presents the se...

Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for correct clustering. Among several existing measures of topology violation, this paper studies the Topographic Function (TF) [1]. We find that this measuring method, demonstrated for low-dimensional data in [1], has a reliable foundation in its distance metric for the interpretation of the neighborhood relations...