scholarly journals 3D Maps Representation Using GNG

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Vicente Morell ◽  
Miguel Cazorla ◽  
Sergio Orts-Escolano ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Input Data ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Growing Neural Gas ◽  
Input Space ◽  
Neural Gas ◽  
Space Experiments ◽  
Space Adaptation

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

Representing Non-Rigid Objects with Neural Networks

2011 ◽  
pp. 1363-1369
Author(s):  
José García-Rodríguez ◽  
Francisco Flórez-Revuelta ◽  
Juan Manuel García-Chamizo
Keyword(s):  
Neural Networks ◽  
Delaunay Triangulation ◽  
Learning Process ◽  
Competitive Learning ◽  
Growing Neural Gas ◽  
Input Space ◽  
Adaptive Process ◽  
Neural Gas ◽  
Rigid Objects

Self-organising neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In this work we use a kind of self-organising network, the Growing Neural Gas, to represent deformations in objects along a sequence of images. As a result of an adaptive process the objects are represented by a topology representing graph that constitutes an induced Delaunay triangulation of their shapes. These maps adapt the changes in the objects topology without reset the learning process.

ONLINE SEMI-SUPERVISED GROWING NEURAL GAS

2012 ◽  
Vol 22 (05) ◽  
pp. 1250023 ◽  
Author(s):  
OLIVER BEYER ◽  
PHILIPP CIMIANO
Keyword(s):  
Supervised Classification ◽  
State Of The Art ◽  
Training Data ◽  
Learning Approaches ◽  
A Posteriori ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Training Examples ◽  
Prediction Functions ◽  
Labeling Strategies

In this paper we introduce online semi-supervised growing neural gas (OSSGNG), a novel online semi-supervised classification approach based on growing neural gas (GNG). Existing semi-supervised classification approaches based on GNG require that the training data is explicitly stored as the labeling is performed a posteriori after the training phase. As main contribution, we present an approach that relies on online labeling and prediction functions to process labeled and unlabeled data uniformly and in an online fashion, without the need to store any of the training examples explicitly. We show that using on-the-fly labeling strategies does not significantly deteriorate the performance of classifiers based on GNG, while circumventing the need to explicitly store training examples. Armed with this result, we then present a semi-supervised extension of GNG (OSSGNG) that relies on the above mentioned online labeling functions to label unlabeled examples and incorporate them into the model on-the-fly. As an important result, we show that OSSGNG performs as good as previous semi-supervised extensions of GNG which rely on offline labeling strategies. We also show that OSSGNG compares favorably to other state-of-the-art semi-supervised learning approaches on standard benchmarking datasets.

scholarly journals Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies

2021 ◽  
Vol 25 (6) ◽  
pp. 1011-1023
Author(s):  
Yuichiro Toda ◽  
◽  
Takayuki Matsuno ◽  
Mamoru Minami
Keyword(s):  
Topological Structure ◽  
Input Data ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Data Distribution ◽  
Growing Neural Gas ◽  
Topological Structures ◽  
Neural Gas ◽  
Batch Learning ◽  
Learning Convergence

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.

A Sparse Representation of High-Dimensional Input Spaces Based on an Augmented Growing Neural Gas

10.29007/jgjt ◽  
2018 ◽  
Author(s):  
Jochen Kerdels ◽  
Gabriele Peters
Keyword(s):  
Euclidean Distance ◽  
Distance Measures ◽  
High Dimensional ◽  
Space Region ◽  
Vector Representation ◽  
Growing Neural Gas ◽  
Input Space ◽  
Neural Gas ◽  
Neighborhood Relations ◽  
Local Input

\noindent The growing neural gas (GNG) algorithm is an unsupervised learning method that is able to approximate the structure of its input space with a network of prototypes. Each prototype represents a local input space region and neighboring prototypes in the GNG network correspond to neighboring regions in input space. Here we address two problems that can arise when using the GNG algorithm. First, the GNG network structure becomes less and less meaningful with increasing dimensionality of the input space as typical distance measures like the Euclidean distance loose their expressiveness in higher dimensions. Second, the GNG itself does not provide a form of output that retains the discovered neighborhood relations when compared with common distance measures. We show that a GNG augmented with {\em local input space histograms} can mitigate both of these problems. We define a sparse vector representation as output of the augmented GNG that preserves important neighborhood relations while pruning erroneous relations that were introduced due to effects of high dimensionality.

scholarly journals TinGa: fast and flexible trajectory inference with Growing Neural Gas

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i66-i74
Author(s):  
Helena Todorov ◽  
Robrecht Cannoodt ◽  
Wouter Saelens ◽  
Yvan Saeys
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
The State ◽  
Data Complexity ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Art Methods ◽  
Model Cell ◽  
Disconnected Graphs ◽  
Developmental Dynamics

Abstract Motivation During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. Results In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date. Availability and implementation R scripts for running TinGa, comparing it to top existing methods and generating the figures of this article are available at https://github.com/Helena-todd/TinGa.

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas

scholarly journals gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

2021 ◽  
Vol 11 (6) ◽  
pp. 2511
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R. Muhammad Atif Azad
Keyword(s):  
State Of The Art ◽  
Heuristic Method ◽  
Good Explanation ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Models ◽  
Boundary Values ◽  
Class Label ◽  
Input Space ◽  
Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

scholarly journals Estimating Efforts and Success of Symmetry-Seeing Machines by Use of Synthetic Data

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 227
Author(s):  
Eckart Michaelsen ◽  
Stéphane Vujasinovic
Keyword(s):  
Extraction Method ◽  
Input Data ◽  
State Of The Art ◽  
Recognition Performance ◽  
Human Subjects ◽  
Synthetic Data ◽  
Ground Truth ◽  
Comparative Test ◽  
Real Imagery ◽  
Dominant Part

Representative input data are a necessary requirement for the assessment of machine-vision systems. For symmetry-seeing machines in particular, such imagery should provide symmetries as well as asymmetric clutter. Moreover, there must be reliable ground truth with the data. It should be possible to estimate the recognition performance and the computational efforts by providing different grades of difficulty and complexity. Recent competitions used real imagery labeled by human subjects with appropriate ground truth. The paper at hand proposes to use synthetic data instead. Such data contain symmetry, clutter, and nothing else. This is preferable because interference with other perceptive capabilities, such as object recognition, or prior knowledge, can be avoided. The data are given sparsely, i.e., as sets of primitive objects. However, images can be generated from them, so that the same data can also be fed into machines requiring dense input, such as multilayered perceptrons. Sparse representations are preferred, because the author’s own system requires such data, and in this way, any influence of the primitive extraction method is excluded. The presented format allows hierarchies of symmetries. This is important because hierarchy constitutes a natural and dominant part in symmetry-seeing. The paper reports some experiments using the author’s Gestalt algebra system as symmetry-seeing machine. Additionally included is a comparative test run with the state-of-the-art symmetry-seeing deep learning convolutional perceptron of the PSU. The computational efforts and recognition performance are assessed.

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