Automatic dimensionality selection from the scree plot via the use of profile likelihood

Zhu, Mu; Ghodsi, Ali

doi:10.1016/j.csda.2005.09.010

Cited by 316 publications

(264 citation statements)

References 6 publications

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“…The optimal model complexity given a proper explained variance (in other words, the optimum number of factors for each mode to be considered), was detected through the scree plot evaluation [29,30] in which models against explained variance % are reported (see Figure 6). The scree plot was realized by starting the calculation from the model with one factor in each mode [1,1,1,1] until the model [5,5,5,3].…”

Section: Four-way Tucker3 Modelmentioning

confidence: 99%

Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring

et al. 2016

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Atmospheric monitoring produces huge amounts of data. Univariate and bivariate statistics are widely used to investigate variations in the parameters. To summarize information graphs are usually used in the form of histograms or tendency profiles (e.g., variable concentration vs. time), as well as bidimensional plots where two-variable correlations are considered. However, when dealing with big data sets at least two problems arise: a great quantity of numbers (statistics) and graphs are produced, and only two-variable interactions are often considered. The aim of this article is to show how the use of multivariate statistics helps in handling atmospheric data sets. Multivariate modeling considers all the variables simultaneously and returns the main results as bidimensional graphs that are easy-to-read. Principal Component Analysis (PCA; the most known multivariate method) and multiway-PCA (Tucker3) are compared from methodological and interpretative points of view. The article demonstrates the ability to emphasize different information depending on the data handling performed. The results and benefits achieved using a more complex model that allows for the simultaneous consideration of the entire variability of the system are compared with the results provided by the simpler but better-known model. Atmospheric monitoring (SO 2 , NO x , NO 2 , NO, and O 3 ) data from the Lake Como Area (Italy) since 1992 to 2007 were chosen for consideration for the case study.

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Section: Four-way Tucker3 Modelmentioning

confidence: 99%

Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring

et al. 2016

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“…In order to reduce the number of tests in this procedure, OTUs were pre-filtered according to their vector lengths calculated from corresponding RDA scores (scaling 1) by profile likelihood selection. 61 Indicator analysis Significant OTUs were further subject to indicator analysis with the R package indicspecies v1.6.5. 62 Indicator OTUs (in analogy to indicator species).…”

Section: Determination Of Significant Otusmentioning

confidence: 99%

Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin

Heinsen

Knecht

Neulinger³

et al. 2015

Gut Microbes

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(2015) Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin, Gut Microbes, 6:4, 243-254, DOI: 10.1080/19490976.2015 To link to this article: https://doi.org/10. 1080/19490976.2015 Keywords: 16S rRNA gene, antibiotics, lumen, microbiota, mucosaGut microbiota play a key role in the host's health system. Broad antibiotic therapy is known to disrupt the microbial balance affecting pathogenic as well as host-associated microbes. The aim of the present study was to investigate the influence of antibiotic paromomycin on the luminal and mucosa-associated microbiota at the DNA (abundance) and RNA (potential activity) level as well as to identify possible differences. The influence of antibiotic treatment on intestinal microbiota was investigated in 5 healthy individuals (age range: 20-22 years). All participants received the antibiotic paromomycin for 3 d. Fecal samples as well as sigmoidal biopsies were collected before and immediately after cessation of antibiotic treatment as well as after a recovery phase of 42 d. Compartment-and treatment statusspecific indicator operational taxonomic units (OTUs) as well as abundance-and activity-specific patterns were identified by 16S rRNA and 16S rRNA gene amplicon libraries and high-throughput pyrosequencing. Microbial composition of lumen and mucosa were significantly different at the DNA compared to the RNA level. Antibiotic treatment resulted in changes of the microbiota, affecting the luminal and mucosal bacteria in a similar way. Several OTUs were identified as compartment-and/or treatment status-specific. Abundance and activity patterns of some indicator OTUs differed considerably. The study shows fundamental changes in composition of gut microbiota under antibiotic therapy at both the potential activity and the abundance level at different treatment status. It may help to understand the complex processes of gut microbiota changes involved in resilience mechanisms and on development of antibiotic-associated clinical diseases.

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“…We use Classical Multidimensional Scaling (CMDS) [13,14] in the embedding. Embedding dimension d = 6 is determined by Zhu and Ghodsi's automatic dimensionality selection [15]. We use ranks of the distances d A based on 200 Monte Carlo simulations to estimate the powers for different levels of α, where the power β α is the probability of rejecting the null hypothesis when rejection is in fact the correct decision and α is the probability of missing a true match.…”

Section: Resultsmentioning

confidence: 99%

Fusion and inference from multiple data sources in a commensurate space

Marchette

Priebe

2012

Statistical Analysis

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Given objects measured under multiple conditions-for example, indoor lighting versus outdoor lighting for face recognition, multiple language translation for document matching, etc.-the challenging task is to perform data fusion and utilize all the available information for inferential purposes. We consider two exploitation tasks: (i) how to determine whether a set of feature vectors represent a single object measured under different conditions; and (ii) how to create a classifier based on training data from one condition in order to classify objects measured under other conditions. The key to both problems is to transform data from multiple conditions into one commensurate space, where the (transformed) feature vectors are comparable and would be treated as if they were collected under the same condition. Toward this end, we studied Procrustes analysis and developed a new approach, which uses the interpoint dissimilarities for each condition. We impute the dissimilarities between measurements of different conditions to create one omnibus dissimilarity matrix, which is then embedded into Euclidean space. We illustrate our methodology on English and French documents collected from Wikipedia, demonstrating superior performance compared to that obtained via standard Procrustes transformation. 

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Automatic dimensionality selection from the scree plot via the use of profile likelihood

Cited by 316 publications

References 6 publications

Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring

Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring

Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin

Fusion and inference from multiple data sources in a commensurate space

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