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Initialization of Self-Organizing Maps: Principal Components Versus Random Initialization. A Case Study [article]

A. A. Akinduko, E. M. Mirkes
2012 arXiv   pre-print
The performance of the Self-Organizing Map (SOM) algorithm is dependent on the initial weights of the map.  ...  In this paper, the performance of random initialization (RI) approach is compared to that of principal component initialization (PCI) in which the initial map weights are chosen from the space of the principal  ...  Introduction Self-Organizing Map (SOM) can be considered as a non-linear generalization of the principal component analysis [14] and has found much application in data exploration especially in data  ... 
arXiv:1210.5873v1 fatcat:dfhd54r3lvgh3mxh7jgou67apa

Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data

Cody R. Goodwin, Stacy D. Sherrod, Christina C. Marasco, Brian O. Bachmann, Nicole Schramm-Sapyta, John P. Wikswo, John A. McLean
2014 Analytical Chemistry  
Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm.  ...  This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps.  ...  Woods (National Institutes of Health-National Institute on Drug Abuse, Baltimore, MD) for initial discussions on the cocaine model investigated.  ... 
doi:10.1021/ac5010794 pmid:24856386 pmcid:PMC4082383 fatcat:z3phqxxhc5ctjlkwkifdjavtjy

Relative Performance of Self-Organizing Maps and Principal Component Analysis in Pattern Extraction from Synthetic Climatological Data

David B. Reusch, Richard B. Alley, Bruce C. Hewitson
2005 Polar Geography  
As a contribution toward improving our ability to identify robust patterns of variability in complex, noisy climate datasets, we have compared a relatively new technique, Self-Organizing Maps (SOMs), to  ...  the well-established method of principal component analysis (PCA).  ...  CONCLUSIONS Self-organizing maps (SOMs) and principal component analysis (PCA) provide distinct but complementary ways to extract and study patterns in climatological and other data.  ... 
doi:10.1080/789610199 fatcat:acfdxjn7p5a37fycuqoh4m74ua

Using Self Organising Maps in Applied Geomorphology [chapter]

Ferentinou Maria, Karymbalis Efthimios, Charou Eleni, Sakellariou Michael
2011 Self Organizing Maps - Applications and Novel Algorithm Design  
The inherent power of self organizing maps to conserve the complexity of the systems they model and self-organize their internal structure was employed, in order to improve knowledge in the field of landscape  ...  Numerical characterizations are used to quantify 15 Self Organizing Maps -Applications and Novel Algorithm Design  ...  (a) Colour code using k-means; (b) Principal component projection; (c) Label map with the names of the alluvial fans, using k-means. .  ... 
doi:10.5772/13265 fatcat:mbufw3eefjdlha77sktkrfhwfq

Self-Organizing Maps with Asymmetric Neighborhood Function

Takaaki Aoki, Toshio Aoyagi
2007 Neural Computation  
The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space  ...  One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training.  ...  This work was supported by CREST (Core Research for Evolutional Science and Technology) of Japan Science and Technology Corporation (JST) and by Grant-in-Aid for Scientific Research from the Ministry of  ... 
doi:10.1162/neco.2007.19.9.2515 pmid:17650068 fatcat:p766kmsxs5gnbpinflbtrmzaka

Photometric Redshift Estimation with Galaxy Morphology using Self-Organizing Maps [article]

Derek Wilson, Hooshang Nayyeri, Asantha Cooray, Boris Häußler
2019 arXiv   pre-print
We use Self Organizing Maps (SOMs) to map the multi dimensional photometric and galaxy size observations while taking advantage of existing spectroscopic redshifts at 0 < z < 2 for independent training  ...  We show that use of photometric and morphological data led to redshift estimates comparable to redshift measurements from SED modeling and from self-organizing maps without morphological measurements.  ...  To obtain a slight improvement in accuracy, the median of the results of 500 SOMs was found (since each self-organizing map will be slightly different as the initial node weights are random and the training  ... 
arXiv:1911.00210v2 fatcat:ue5kqykmlzhw3ivrotktysnl4m

Performance evaluation of the self-organizing map for feature extraction

Yonggang Liu, Robert H. Weisberg, Christopher N. K. Mooers
2006 Journal of Geophysical Research  
1] Despite its wide applications as a tool for feature extraction, the Self-Organizing Map (SOM) remains a black box to most meteorologists and oceanographers.  ...  The SOM is shown to extract the patterns of a linear progressive sine wave. Sensitivity studies are performed to ascertain the effects of the SOM tunable parameters.  ...  Support was by the Office of Naval Research, grants N00014-98-1-0158 and N00014-02-1-0972.  ... 
doi:10.1029/2005jc003117 fatcat:6gqjqtcdrbhelhh6lalprot3d4

Self-Organizing Maps to Analyze Value Creation in Mergers and Acquisitions in the Telecommunications Sector [chapter]

Julio Navío, Jose M. Martinez-Martinez, Alberto Urueña, Juan J. Garcés, Emilio Soria
2017 Emerging Issues in Economics and Development  
In this work, we use a visual data-mining tool, Self-Organizing-Maps (SOM), to analyze mergers and acquisitions in telecommunications sector.  ...  of merger and acquisition (M&A) in the sector of telecommunications.  ...  On one hand, random initialization and on the other, principal component analysis of the inputs was used. • Neighborhood function.  ... 
doi:10.5772/intechopen.68757 fatcat:n6ysud3exjfs7j7fhu7lqtlerq

On pattern classification with Sammon's nonlinear mapping an experimental study

Boaz Lerner, Hugo Guterman, Mayer Aladjem, Its'hak Dinsteint, Yitzhak Romem
1998 Pattern Recognition  
The projection map and classification accuracy of the mapping are compared with those of the auto-associative NN (AANN), multilayer perceptron (MLP) and principal component (PC) feature extractor for chromosome  ...  In this paper we apply a neural network (NN) implementation of Sammon's mapping to classification by extracting an arbitrary number of projections.  ...  Kohonen's self-organizing map (SOM) (6) is another example of an unsupervised nonlinear projection method based on an NN.  ... 
doi:10.1016/s0031-3203(97)00064-2 fatcat:bzrssimagbhjpay6pfxybxvjha

Social Cognitive Maps, Swarm Perception and Distributed Search on Dynamic Landscapes [article]

Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa
2005 arXiv   pre-print
KEYWORDS: Swarm Intelligence and Perception, Social Cognitive Maps, Social Foraging, Self-Organization, Distributed Search and Optimization  ...  To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to self-organization, stigmergy and social foraging in animals.  ...  A paradigmatic case is provided by the emergence of self-organization in social insects, by way of indirect interactions.  ... 
arXiv:nlin/0502057v1 fatcat:a34fjabnofapjpcy457estx7wq

Coupling self-organizing maps with a Naïve Bayesian classifier: Stream classification studies using multiple assessment data

Nikolaos Fytilis, Donna M. Rizzo
2013 Water Resources Research  
In this work, we develop a new classification tool that couples a Na€ ıve Bayesian classifier with a neural network clustering algorithm (i.e., Kohonen Self-Organizing Map (SOM)).  ...  Organizing or clustering data into natural groups is one of the most fundamental aspects of understanding and mining information.  ...  This research was funded in part by NSF grant 1216193 as part of the joint NSF-NIH-USDA Ecology and  ... 
doi:10.1002/2012wr013422 fatcat:7c54rzh7vjdtvd7ttbjlor3c5i

Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity

Shing-Chung Ngan, Xiaoping Hu
1999 Magnetic Resonance in Medicine  
In this work, Kohonen's self-organizing mapping (SOM), which is a model-free approach, is adapted for analyzing fMRI data.  ...  The applicability of the new algorithm is demonstrated on experimental data. Magn Reson Med 41:939  ...  Chen for the implementation of the standard SOM algorithm; K. Heberlein, S. LaConte, W. Molitor, S. Sarkar, and E. Yacoub for their helpful suggestions and comments; and Drs. S.-G. Kim and C.  ... 
doi:10.1002/(sici)1522-2594(199905)41:5<939::aid-mrm13>;2-q pmid:10332877 fatcat:2wlxmgbra5fkrjh5cifvtjneyy

Augmenting Weak Semantic Cognitive Maps with an "Abstractness" Dimension

Alexei V. Samsonovich, Giorgio A. Ascoli
2013 Computational Intelligence and Neuroscience  
The notion of weak semantic maps was introduced recently as distribution of representations in abstract spaces that are not derived from human judgments, psychometrics, or any other a priori information  ...  Specifically, including hyponym-hypernym relations yields a new semantic dimension of the map labeled here "abstractness" (or ontological generality) that is not reducible to any dimensions represented  ...  Thomas Sheehan for help with extraction of the relation data from WordNet. Part of Giorgio A.  ... 
doi:10.1155/2013/308176 pmid:23840200 pmcid:PMC3694378 fatcat:nfevr4c4obht7g4hzwogphwcq4

A theoretical solution to MAP-EM partial volume segmentation of medical images

Su Wang, Hongyu Lu, Zhengrong Liang
2009 International journal of imaging systems and technology (Print)  
When modeling each tissue type as a conditionally independent Gaussian distribution, the tissue mixture fractions in each voxel via the modeled unobservable random processes of the underlying tissue types  ...  can be estimated by maximum a posteriori expectationmaximization (MAP-EM) algorithm in an iterative manner.  ...  In other words, each of those voxels of class "3" now has a 5-D feature vector, where each dimension reflects a distinct principle component. • Via unsupervised self-adaptive VQ classification [16] scheme  ... 
doi:10.1002/ima.20187 pmid:19768123 pmcid:PMC2745964 fatcat:24x4nt2ef5hg5daslgcmxph76m

hiHMM: Bayesian non-parametric joint inference of chromatin state maps

Kyung-Ah Sohn, Joshua W. K. Ho, Djordje Djordjevic, Hyun-hwan Jeong, Peter J. Park, Ju Han Kim
2015 Computer applications in the biosciences : CABIOS  
A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome.  ...  This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them.  ...  This is evident from our 25 state analyses in fly versus worm in case study 1.  ... 
doi:10.1093/bioinformatics/btv117 pmid:25725496 pmcid:PMC4481846 fatcat:nnafrz7sf5dfdk5qliqtrwwfcm
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