History of paintingThe history of painting reaches back in time to artifacts and artwork created by pre-historic artists, and spans all cultures. It represents a continuous, though periodically disrupted, tradition from Antiquity. Across cultures, continents, and millennia, the history of painting consists of an ongoing river of creativity that continues into the 21st century. Until the early 20th century it relied primarily on representational, religious and classical motifs, after which time more purely abstract and conceptual approaches gained favor.
Western paintingThe history of Western painting represents a continuous, though disrupted, tradition from antiquity until the present time. Until the mid-19th century it was primarily concerned with representational and Classical modes of production, after which time more modern, abstract and conceptual forms gained favor. Initially serving imperial, private, civic, and religious patronage, Western painting later found audiences in the aristocracy and the middle class. From the Middle Ages through the Renaissance painters worked for the church and a wealthy aristocracy.
Nonlinear dimensionality reductionNonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Dimensionality reductionDimensionality 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 (hard to control or deal with).
Data processingData processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant.
Four-dimensional spaceFour-dimensional space (4D) is the mathematical extension of the concept of three-dimensional space (3D). Three-dimensional space is the simplest possible abstraction of the observation that one needs only three numbers, called dimensions, to describe the sizes or locations of objects in the everyday world. For example, the volume of a rectangular box is found by measuring and multiplying its length, width, and height (often labeled x, y, and z).
Aspect ratio (image)The aspect ratio of an image is the ratio of its width to its height, and is expressed with two numbers separated by a colon, such as 16:9, sixteen-to-nine. For the x:y aspect ratio, the image is x units wide and y units high. Common aspect ratios are 1.85:1 and 2.39:1 in cinematography, 4:3 and 16:9 in television photography, and 3:2 in still photography. The common film aspect ratios used in cinemas are 1.85:1 and 2.39:1. Two common videographic aspect ratios are 4:3 (1.:1), the universal video format of the 20th century, and 16:9 (1.
Thought experimentA thought experiment is a hypothetical situation in which a hypothesis, theory, or principle is laid out for the purpose of thinking through its consequences. The ancient Greek deiknymi, "was the most ancient pattern of mathematical proof", and existed before Euclidean mathematics, where the emphasis was on the conceptual, rather than on the experimental part of a thought-experiment. Johann Witt-Hansen established that Hans Christian Ørsted was the first to use the term Gedankenexperiment (from German: 'thought experiment') circa 1812.
