Respuesta :
Answer:
Following are the difference to this question can be defined as follows:
Explanation:
- In terms of projections of information into a reduced dimension group, we can consider the dimension structure of PCA and SVD technologies.
- In cases of a change in the length, based on consolidation, there's also a unit with dimensions.
- When we consider that the days have been aggregated by days or the trade of a commodity can be aggregated to both the area of the dimension, accumulation can be seen with a variance of dimension.
Dimensionality reduction based on aggregation involves selection of important character and variable from a large variable and dimensionality reduction based on techniques with the use of PCA and SVD.
What is dimensionality?
Dimensionality involves reducing features that have been written or identified or constructing less features from a pool of important features.
Principal components analysis(PCA) and Single value decomposition(SVD) is a form or analysis that can be used to reduce some character that are not important to an information and can also help to select important variables from among many variables.
Therefore, dimensionality reduction based on aggregation involves selection of important character and variable from a large variable and dimensionality reduction based on techniques with PCA and SVD.
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