2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)最新文献_第3页

Incremental learning with self-organizing maps 使用自组织地图的增量学习

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020021

A. Gepperth, Cem Karaoguz

{"title":"Incremental learning with self-organizing maps","authors":"A. Gepperth, Cem Karaoguz","doi":"10.1109/WSOM.2017.8020021","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020021","url":null,"abstract":"We present a novel use for self-organizing maps (SOMs) as an essential building block for incremental learning algorithms. SOMs are very well suited for this purpose because they are inherently online learning algorithms, because their weight updates are localized around the best-matching unit, which inherently protects them against catastrophic forgetting, and last but not least because they have fixed model complexity limiting execution time and memory requirements for processing streaming data. However, in order to perform incremental learning which is usually supervised in nature, SOMs need to be complemented by a readout layer as well as a self-referential control mechanism for prototype updates in order to be protected against negative consequences of concept drift. We present the PROPRE architecture which implements these functions, thus realizing incremental learning with SOMs in very high-dimensional data domains, and show its capacity for incremental learning on several known and new classification problems. In particular, we discuss the required control of SOM parameters in detail and validate our choices by experimental results.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121941691","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

Using self-organizing maps for clustering anc labelling aircraft engine data phases 使用自组织地图对飞机发动机数据阶段进行聚类和标记

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020013

Cynthia Faure, Madalina Olteanu, J. Bardet, J. Lacaille

{"title":"Using self-organizing maps for clustering anc labelling aircraft engine data phases","authors":"Cynthia Faure, Madalina Olteanu, J. Bardet, J. Lacaille","doi":"10.1109/WSOM.2017.8020013","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020013","url":null,"abstract":"Multiple signals are measured by sensors during a flight or a test bench and their analysis represent a big interest for engineers. These signals are actually multivariate time series created by the sensors present on the aircraft engines. Each of them can be decomposed into series of stabilized phases, well known by the experts, and transient phases. Transient phases are merely explored but they reveal a lot of information when the engine is running. The aim of our project is converting these time series into a succession of labels, designing transient and stabilized phases. This transformation of the data will allow to derive several perspectives: on one hand, tracking similar behaviours or patterns seen during a flight; on the other, discovering hidden structures. Labelling signals coming from the engines of the aircraft also helps in the detection of frequent or rare sequences during a flight. Statistical analysis and scoring are more convenient with this new representation. This manuscript proposes a methodology for automatically indexing all engine transient phases. First, the algorithm computes the start and the end points of each phase and builds a new database of transient patterns. Second, the transient patterns are clustered into a small number of typologies, which will provide the labels. The clustering is implemented with Self-Organizing Maps [SOM]. All algorithms are applied on real flight measurements with a validation of the results from expert knowledge.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"97 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127996627","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: 15

Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities 基于数据的最接近原型矢量量化器鉴别能力差异评价

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020030

M. Kaden, D. Nebel, F. Melchert, Andreas Backhaus, U. Seiffert, T. Villmann

引用次数: 0

Small sets of random Fourier features by kernelized Matrix LVQ 小集合随机傅立叶特征的核化矩阵LVQ

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020026

Frank-Michael Schleif

引用次数: 3

Stochastic self-organizing map variants with the R package SOMbrero 随机自组织映射变体与R包SOMbrero

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020014

N. Villa-Vialaneix

引用次数: 4

Probabilistic extension and reject options for pairwise LVQ 成对LVQ的概率扩展和拒绝选项

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020028

Johannes Brinkrolf, B. Hammer

引用次数: 4

Using spatial characteristics to aid automation of SOM segmentation of functional image data 利用空间特征辅助功能图像数据的SOM分割自动化

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020012

Patrick O'Driscoll, E. Merényi, R. Grossman

{"title":"Using spatial characteristics to aid automation of SOM segmentation of functional image data","authors":"Patrick O'Driscoll, E. Merényi, R. Grossman","doi":"10.1109/WSOM.2017.8020012","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020012","url":null,"abstract":"We propose a new similarity measure, Combined Connectivity and Spatial Adjacency (CCSA), to be used in hierarchical agglomerative clustering (HAC) for automated segmentation of Self-Organizing Maps (SOMs, Kohonen [1]). The CCSA measure is specifically designed to assist segmentation of large, complex, functional image data by exploiting general spatial characteristics of such data. The proposed CCSA measure is constructed from two strong indicators of cluster structure: the degree of localization of data points in physical space and the degree of connectivity of SOM prototypes (as defined by Taçdemir and Merényi [2]). The new measure is expected to enhance cluster capture in large functional image data cubes such as hyperspectral imagery or fMRI brain images, where many relevant clusters exist with widely varying statistical properties and in complex relationships both in feature space and in physical (image) space. We demonstrate the effectiveness of our approach using the CCSA measure on progressively complex synthetic spatial data and on real fMRI brain data.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123731876","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

Pairwise elastic self-organizing maps 成对弹性自组织映射

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020006

P. Hartono, Yuto Take

引用次数: 3

Detection of short circuit faults in 3-phase converter-fed induction motors using kernel SOMs 基于内核SOMs的三相变频异步电动机短路故障检测

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-06-01 DOI: 10.1109/WSOM.2017.8020016

D. N. Coelho, G. Barreto, Cláudio M. S. Medeiros

{"title":"Detection of short circuit faults in 3-phase converter-fed induction motors using kernel SOMs","authors":"D. N. Coelho, G. Barreto, Cláudio M. S. Medeiros","doi":"10.1109/WSOM.2017.8020016","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020016","url":null,"abstract":"In this work we report the results of a comprehensive study involving the application of kernel self-organizing maps (KSOM) for early detection of interturn short-circuit faults in a three-phase converter-fed induction motor. For this purpose, two paradigms for developing KSOM-based classifiers are evaluated on the problem of interest, namely the gradient descent based KSOM (GD-KSOM) and the energy function based KSOM (EF-KSOM). Their performances are contrasted on a real-world dataset generated by means of a laboratory scale testbed that allows the simulation of different levels of interturn short-circuits (high and low impedance) for different load conditions. Feature vectors are built from the FFT-based spectrum analysis of the stator current, a non-invasive method known as the stator current signature. The performances of the aforementioned KSOM paradigms are evaluated for different kernel functions and for different neuron labeling strategies. The obtained results are compared with those achieved by standard SOM-based classifier.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129043353","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: 7

Multidimensional urban segregation: An exploratory case study 多维城市隔离:一个探索性案例研究

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Pub Date : 2017-05-09 DOI: 10.1109/WSOM.2017.8020024

M. Cottrell, Madalina Olteanu, J. Randon-Furling, Aurélien Hazan

{"title":"Multidimensional urban segregation: An exploratory case study","authors":"M. Cottrell, Madalina Olteanu, J. Randon-Furling, Aurélien Hazan","doi":"10.1109/WSOM.2017.8020024","DOIUrl":"https://doi.org/10.1109/WSOM.2017.8020024","url":null,"abstract":"Segregation phenomena have long been a concern for policy makers and urban planners, and much attention has been devoted to their study, especially in the fields of quantitative sociology and geography. Perhaps the most common example of urban segregation corresponds to different groups living in different neighbourhoods across a city, with very few neighbourhoods where all groups are represented in roughly the same proportions as in the whole city itself. The social groups in question are usually defined according to one variable: ethnic group, income category, religious group, electoral group, age… In this paper, we introduce a novel, multidimensional approach based on the Self-Organizing Map algorithm (SOM). Working with public data available for the city of Paris, we illustrate how this method allows one to describe the complex interplay between social groups' residential patterns and the geography of metropolitan facilities and services. Further, this paves the way to the definition of a robust segregation index through a comparison between the Kohonen map and the actual geographical map.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115190518","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: 5