What is the relationship between high and low dimensionality?
High/low dimensionality is associated with ratio between observations and features in data set. In case, the number of observations is significantly lower than the number of features it is considered high dimensional data set. Is this answer outdated?
What makes a data set high or low dimensional?
High/low dimensionality is associated with ratio between observations and features in data set. In case, the number of observations is significantly lower than the number of features it is considered high dimensional data set. Thanks for contributing an answer to Stack Overflow!
How is high dimensional data defined in machine learning?
So high dimensional data isn’t actually about a large number of features (as the accepted answer suggests), it is defined by the features/samples ratio. Note that this definition holds for the machine learning community, but may not relate to the same idea in other fields.
How is missing data handled in manifold learning?
In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for missing data in PCA. In manifold learning, the presence of noise in the data can “short-circuit” the manifold and drastically change the embedding.
What do you mean by dimensionality reduction in physics?
For dimensional reduction in physics, see dimensional 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.
How is Feature projection used in dimensionality reduction?
Feature projection (also called Feature extraction) transforms the data from the high-dimensional space to a space of fewer dimensions. The data transformation may be linear, as in principal component analysis (PCA), but many nonlinear dimensionality reduction techniques also exist.