Principal Components Analysis Competitive Learning

2003 ◽  
pp. 318-325 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
José Muñoz-Pérez ◽  
José Antonio Gómez-Ruiz
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Competitive Learning ◽  
Components Analysis

Principal Components Analysis Competitive Learning

2004 ◽  
Vol 16 (11) ◽  
pp. 2459-2481 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
José Muñoz-Pérez ◽  
José Antonio Gómez-Ruiz
Keyword(s):  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Neural Model ◽  
Competitive Learning ◽  
Experimental Results ◽  
Data Set ◽  
Input Distribution ◽  
Local Pca ◽  
Components Analysis

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Bootstrapping Principal Components Analysis: Reply to Mehlman Et Al.

Ecology ◽  
1995 ◽  
Vol 76 (2) ◽  
pp. 644-645 ◽  
Author(s):  
Donald A. Jackson
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis

Assamese vowels and vowel harmony

2020 ◽  
Vol 6 (2) ◽  
pp. 151-183
Author(s):  
Diana B. Archangeli ◽  
Jonathan Yip
Keyword(s):  
Physical Properties ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Vowel Harmony ◽  
Language Community ◽  
Acoustic Data ◽  
Apparent Loss ◽  
Root Position ◽  
Components Analysis ◽  
Open Question

AbstractBased on impressionistic and acoustic data, Assamese is described as having a phonological tongue root harmony system, with blocking by certain phonological configurations and over-application in certain morphological contexts. This study explores physical properties of the patterns using ultrasonic imaging to determine whether the impressionistic descriptions match what speakers actually do. Principal components analysis (PCA) determines that most participants produce a contrast in tongue root position in the appropriate contexts, though there is less of an impact on tongue root with greater distance from the triggering vowel. Analysis uses the root mean squared distance (RMSD) calculation to determine whether both blocking and over-application take effect. The blocking results conform to the impressionistic descriptions. With over-application, [e] and [o] are expected; while some speakers clearly produce these vowels, others articulate a vowel that is indeterminant between the expected [e]/[o] and an unexpected [ɛ]/[ɔ]. No speaker consistently showed the expected tongue root position in all contexts, and some speakers appeared to have lost the contrast entirely, yet all are considered to be speakers of the same dialect of Assamese. Whether this (apparent) loss is a consequence of crude research methodologies or accurately reflects what is happening within the language community remains an open question.

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