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This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to...
Separate processing of local luminance and contrast in biological visual systems has been argued to be due to the independence of these two properties in natural image data. In this paper we examine spatial, retinotopic channels formed by these two quantities and use Independent Component Analysis to study the possible dependencies between the channels. As a result, oriented, localized bandpass filter...
The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to generative topographic mapping (GTM), a manifold learning constrained mixture model that provides...
Pattern mining gains more and more attention due to its useful applications in many areas, such as machine learning, database, multimedia, biology, and so on. Though there exist a lot of approaches for pattern mining, few of them consider the local distribution of the data. In the paper, we not only design six challenge datasets related to the local patterns, but also propose a new pattern mining...
We propose an approach to improve the accuracy of estimating feature points of human body on a vision-based motion capture system (MCS) by using the Variable-Density Self-Organizing Map (VDSOM). The VDSOM is a kind of Self-Organizing Map (SOM) and has an ability to learn training samples incrementally. We let VDSOM learn 3-D feature points of human body when the MCS succeeded in estimating them correctly...
Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by...
Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These...
High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly...
This paper presents a new Self-organizing Map suitable for recovering a 2D surface starting from points sampled on the object surface. Growing self-organizing surface map (GSOSM), is a new algorithm of the growing SOM family that reproduce the surface as an incremental mesh composed of triangles which are approximately equilateral. GSOSM introduces a new connection learning rule, called competitive...
The self-organizing map (SOM) is an unsupervised neural network approach that reduces a high-dimensional data set to a representative and compact two-dimensional grid. In so doing, a SOM reveals emergent clusters within the data. Research has shown that SOMs lend themselves to visual and computational analysis for exploratory and data mining purposes. However, an important requirement for many SOM...
With the fast development of robotics and intelligent vehicles, there has been much research work on modeling and motion control of autonomous vehicles. However, due to model complexity, and unknown disturbances from dynamic environment, the motion control of autonomous vehicles is still a difficult problem. In this paper, a novel self-learning path-tracking control method is proposed for a car-like...
The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain...
This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10...
Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications...
The work presented in this paper merges the Bienenstock-Cooper-Munro (BCM) learning rule with spike timing dependent plasticity (STDP) to develop a training algorithm for a spiking neural network (SNN), stimulated using spike trains. The BCM rule is utilised to modulate the height of the plasticity window, associated with STDP. The SNN topology uses a single training neuron in the training phase where...
A novel wake-sleep learning architecture for processing a robotpsilas facial expressions is introduced. According to neuroscience evidence, associative learning of emotional responses and facial expressions occurs in the brain in the amygdala. Here we propose an architecture inspired by how the amygdala receives information from other areas of the brain to discriminate it and generate innate responses...
Self organizing map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition...
In earlier work, we derived the dynamical behavior of a network of oscillatory units described by the amplitude and phase of oscillations. The dynamics were derived from an objective function that rewards both the faithfulness and the sparseness of representation. After unsupervised learning, the network is capable of separating mixtures of inputs, and also segmenting the inputs into components that...
Despite considerable advances over the last few decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. The fundamental reason for this being the mismatch between the growing diversity and complexity of machinery and equipment employed in industry and...
A challenging task is to classify Internet customers based on their heterogeneous search histories of shopping in the Internet. The problem is the data pattern itself. Each transition of a customer from one page to the next in purchasing a commodity is considered as an attribute and this is a pair of data. The purchase patterns consist of usually different length for different customers. We cannot...
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