Transformation Operation
(Redirected from Transformation (Function))
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A Transformation Operation is a set operation/set function that maps a data set into some other data set.
- AKA: Encode, Parametrize, Parameterize, Transform Function.
- Context:
- It can range from being a Discrete Transform to being a Continuous Transform.
- It can be a Reflection Operation.
- It can be a Translation Operation.
- It can be expressed in a Transformation Language.
- Example(s):
- a Linear Transform.
- an Affine Transform.
- a Fourier Transform.
- a Gabor Transform.
- …
- Counter-Example(s):
- See: Algebra, Geometric, Morphism, Rotation, Euclidean Space, Linear Algebra, Transformation Task, Encoding Task.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/transformation_(function) Retrieved:2014-7-25.
- In mathematics, a 'transformation could be any function mapping a set X to another set or to itself. However, often the set X has some additional algebraic or geometric structure and the term "transformation" refers to a function from X to itself that preserves this structure.
Examples include linear transformations and affine transformations, rotations, reflections and translations. These can be carried out in Euclidean space, particularly in dimensions 2 and 3. They are also operations that can be performed using linear algebra, and described explicitly using matrices.
- In mathematics, a 'transformation could be any function mapping a set X to another set or to itself. However, often the set X has some additional algebraic or geometric structure and the term "transformation" refers to a function from X to itself that preserves this structure.
2009
- (Chen et al., 2009) ⇒ Bo Chen, Wai Lam, Ivor Tsang, and Tak-Lam Wong. (2009). “Extracting Discrimininative Concepts for Domain Adaptation in Text Mining.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557045
- … Consequently a good feature representation can encode this concept space and minimize the distribution gap. To formalize this intuition, we propose a domain adaptation method that parameterizes this concept space by linear transformation under which we explicitly minimize the distribution difference between the source domain with sufficient labeled data and target domains with only unlabeled data ...