2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)最新文献

Empirical evaluation of gradient methods for matrix learning vector quantization 梯度方法在矩阵学习向量量化中的经验评价

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-08-31 DOI: 10.1109/WSOM.2017.8020027

Michael LeKander, Michael Biehl, Harm de Vries

引用次数: 8

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning 融合深度学习架构、多层前馈网络和深度分类学习的学习向量量化器

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-08-31 DOI: 10.1109/WSOM.2017.8020009

T. Villmann, Michael Biehl, A. Villmann, S. Saralajew

{"title":"Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning","authors":"T. Villmann, Michael Biehl, A. Villmann, S. Saralajew","doi":"10.1109/WSOM.2017.8020009","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020009","url":null,"abstract":"The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures of multilayer networks achieve frequently very high accuracies and are, thanks to modern graphic processor units use for calculation, trainable in acceptable time. In this conceptual paper we show, how we can combine both network architectures to benefit from their advantages. For this purpose, we consider learning vector quantizers in terms of feedforward network architectures and explain how it can be combined effectively with multilayer or single-layer feedforward network architectures. This approach includes deep and flat architectures as well as the popular extreme learning machines. For the resulting networks, the multi-/single-layer networks act as adaptive filters like in signal processing while the interpretability of the prototype-based learning vector quantizers is kept for the resulting filtered feature space. In this way a powerful combination of two successful architectures is obtained.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115557307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Prototypes and matrix relevance learning in complex fourier space 复傅立叶空间中的原型与矩阵相关学习

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-08-31 DOI: 10.1109/WSOM.2017.8020019

M. Straat, M. Kaden, M. Gay, T. Villmann, A. Lampe, U. Seiffert, Michael Biehl, F. Melchert

引用次数: 3

Motivated self-organization 动机的自我组织

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020000

N. Rougier, Y. Boniface

引用次数: 0

Credible visualizations for planar projections 可信的平面投影可视化

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020010

A. Ultsch, Michael C. Thrun

引用次数: 18

Improving individual predictions using social networks assortativity 利用社交网络分类性改进个人预测

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020023

D. Mulders, Cyril de Bodt, Johannes Bjelland, A. Pentland, M. Verleysen, Yves-Alexandre de Montjoye

{"title":"Improving individual predictions using social networks assortativity","authors":"D. Mulders, Cyril de Bodt, Johannes Bjelland, A. Pentland, M. Verleysen, Yves-Alexandre de Montjoye","doi":"10.1109/WSOM.2017.8020023","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020023","url":null,"abstract":"Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations, when data are missing or inaccurate. This paper presents a general framework based on probabilistic graphical models to exploit social network structures for improving individual predictions of node attributes. Using this framework, we quantify the assortativity range leading to an accuracy gain in several situations. We finally show how specific characteristics of the network can improve performances further. For instance, the gender assortativity in real-world mobile phone data changes significantly according to some communication attributes. In this case, individual predictions with 75% accuracy are improved by up to 3%.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124306036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

An evolutionary building algorithm for Deep Neural Networks 深度神经网络的进化构建算法

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020002

R. Zemouri

引用次数: 8

Imputation of reactive silica and available alumina in bauxites by self-organizing maps 用自组织图法测定铝土矿中活性二氧化硅和有效氧化铝

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020008

C. C. Carneiro, Dayana Niazabeth Del Valle Silva Yanez, C. Ulsen, S. Fraser, Juliana Livi Antoniassi, S. Paz, R. Angélica, H. Kahn

{"title":"Imputation of reactive silica and available alumina in bauxites by self-organizing maps","authors":"C. C. Carneiro, Dayana Niazabeth Del Valle Silva Yanez, C. Ulsen, S. Fraser, Juliana Livi Antoniassi, S. Paz, R. Angélica, H. Kahn","doi":"10.1109/WSOM.2017.8020008","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020008","url":null,"abstract":"Geochemical analyses can provide multiple analytical variables. Accordingly, the generation of large geochemical databases enables imputation studies or analytical estimates of missing values or complex measuring. The processing of bauxite is a key step in the production of aluminum, in which the determination of Reactive Silica (RxSiO<inf>2</inf>) and Available Alumina (AvAl<inf>2</inf>O<inf>3</inf>) are very relevant. The traditional analytical method for achieving RxSiO<inf>2</inf> has limitations associated with poor repeatability and reproducibility of results. Based on the values from the unsupervised Self-Organizing Maps technique, this study aims to develop, systematically, the imputation of missing grades of the geochemical composition of bauxite samples of a database from three trial projects, for the variables: total Al<inf>2</inf>O<inf>3</inf>; total SiO<inf>2</inf>; total Fe<inf>2</inf>O<inf>3</inf>; and total TiO<inf>2</inf>. Each project was submitted to partial exclusion of AvAl<inf>2</inf>O<inf>3</inf> and RxSiO<inf>2</inf> values, in proportion of 20%, 30%, 40% and 50%, to investigate the SOM technique as imputation method for RxSiO<inf>2</inf> and AvAl<inf>2</inf>O<inf>3</inf>. By comparing the imputed values from the SOM analysis with the original values, SOM technique demonstrated to be an imputation tool capable of obtaining analytical results with up to 50% of missing data. Specifically, the best results demonstrate that AvAl<inf>2</inf>O<inf>3</inf> can be obtained by imputation with a higher correlation than RxSiO<inf>2</inf>, based on the parameters and variables involved in the study. Similarity in the nature of samples and an increase in the number of embedded analytical variables are factors that provided better imputation results.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"50 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113934161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SOM-empowered graph segmentation for fast automatic clustering of large and complex data 基于som的图形分割，用于大型复杂数据的快速自动聚类

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020004

E. Merényi, Joshua Taylor

{"title":"SOM-empowered graph segmentation for fast automatic clustering of large and complex data","authors":"E. Merényi, Joshua Taylor","doi":"10.1109/WSOM.2017.8020004","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020004","url":null,"abstract":"Many clustering methods, including modern graph segmentation algorithms, run into limitations when encountering “Big Data”, data with high feature dimensions, large volume, and complex structure. SOM-based clustering has been demonstrated to accurately capture many clusters of widely varying statistical properties in such data. While a number of automated SOM segmentations have been put forward, the best identifications of complex cluster structures to date are those performed interactively from informative visualizations of the learned SOM's knowledge. This does not scale for Big Data, large archives or near-real time analyses for fast decision-making. We present a new automated approach to SOM-segmentation which closely approximates the precision of the interactive method for complicated data, and at the same time is very fast and memory-efficient. We achieve this by infusing SOM knowledge into leading graph segmentation algorithms which, by themselves, produce extremely poor results segmenting the SOM prototypes. We use the SOM prototypes as input vectors and CONN similarity measure, derived from the SOM's knowledge of the data connectivity, as edge weighting to the graph segmentation algorithms. We demonstrate the effectiveness on synthetic data and on real spectral imagery.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134153102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Self-organizing map for orienteering problem with dubins vehicle 杜宾车辆定向问题的自组织地图

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-28 DOI: 10.1109/WSOM.2017.8020017

J. Faigl

{"title":"Self-organizing map for orienteering problem with dubins vehicle","authors":"J. Faigl","doi":"10.1109/WSOM.2017.8020017","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020017","url":null,"abstract":"This paper reports on the application of the self-organizing map (SOM) to solve a novel generalization of the Orienteering Problem (OP) for curvature-constrained vehicles that is called the Dubins Orienteering Problem (DOP). Having a set of target locations, each with associated reward, and a given travel budget, the problem is to find the most valuable curvature-constrained path connecting the target locations such that the path does not exceed the travel budget. The proposed approach is based on two existing SOM-based approaches to solving the OP and Dubins Traveling Salesman Problem (Dubins TSP) that are further generalized to provide a solution of the more computational challenging DOP. DOP combines challenges of the combinatorial optimization of the OP and TSP to determine a subset of the most valuable targets and the optimal sequence of the waypoints to collect rewards of the targets together with the continuous optimization of determining headings of Dubins vehicle at the waypoints such that the total length of the curvature-constrained path is shorter than the given travel budget and the total sum of the collected rewards is maximized.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4