Exploratory Factor Analysis (EFA)
In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables. It is commonly used by researchers when developing a scale (a scale is a collection of questions used to measure a particular research topic) and serves to identify a set of latent constructs underlying a battery of measured variables. It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables. Measured variables are any one of several attributes of people that may be observed and measured. An example of a measured variable would be the physical height of a human being. Researchers must carefully consider the number of measured variables to include in the analysis. EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis. EFA is based on the common factor model. Within the common factor model, a function of common factors, unique factors, and errors of measurements expresses measured variables. Common factors influence two or more measured variables, while each unique factor influences only one measured variable and does not explain correlations among measured variables. EFA assumes that any indicator/measured variable may be associated with any factor. When developing a scale, researchers should use EFA first before moving on to confirmatory factor analysis (CFA). EFA requires the researcher to make a number of important decisions about how to conduct the analysis because there is no one set method.
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Superpixels
Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives. As of these properties, superpixel algorithms have received much attention since their naming in 2003. By today, publicly available and well-understood superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new and relevant insights. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/. …

Aggregation Operator
Aggregation operators are mathematical functions that are used to combine information. That is, they are used to combine N data (for example, N numerical values) in a single datum. The arithmetic mean and the weighted mean are the most well-known aggregation operators. The median and the mode can also been as aggregation operators. The main difference between the arithmetic mean and the weighted mean is that the latter permits us to weight the different data according to their relevance. There exist a large number of different aggregation operators that differ on the assumptions on the data (data types) and about the type of information that we can incorporate in the model. For example, fuzzy integrals permits us to assign relevance to sets of information sources and not only to individual sources as for the weighted mean. …