Dimensionality reduction in the context of "Big Five personality traits"

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⭐ Core Definition: Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable. Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics.

Methods are commonly divided into linear and nonlinear approaches. Linear approaches can be further divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate step to facilitate other analyses.

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👉 Dimensionality reduction in the context of Big Five personality traits

In psychology and psychometrics, the big five personality trait model or five-factor model (FFM)—sometimes called by the acronym OCEAN or CANOE—is a scientific model for measuring and describing human personality traits. The framework groups variation in personality into five separate factors, all measured on a continuous scale:

The five-factor model was developed using empirical research into the language people used to describe themselves, which found patterns and relationships between the words people use to describe themselves. For example, because someone described as "hard-working" is more likely to be described as "prepared" and less likely to be described as "messy", all three traits are grouped under conscientiousness. Using dimensionality reduction techniques, psychologists showed that most (though not all) of the variance in human personality can be explained using only these five factors.

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Dimensionality reduction in the context of Linear discriminant analysis

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

LDA is closely related to analysis of variance (ANOVA) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. However, ANOVA uses categorical independent variables and a continuous dependent variable, whereas discriminant analysis has continuous independent variables and a categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain a categorical variable by the values of continuous independent variables. These other methods are preferable in applications where it is not reasonable to assume that the independent variables are normally distributed, which is a fundamental assumption of the LDA method.

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LINKED FROM (PARENTS)
← Big Five personality traits ← Linear discriminant analysis
LINKS TO (CHILDREN)
Intrinsic dimension → Sparse matrix → Curse of dimensionality → Computational complexity theory → Signal processing → Speech recognition → Neuroinformatics → Bioinformatics → Feature selection → Feature extraction → Noise reduction → Data visualization → Cluster analysis →