Examples of using Dimensionality in English and their translations into Chinese
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Programming
The way to deal with it is to fix the dimensionality at the maximum possible sequence length and pad the unused places with zeros.
Sample application demonstrating how to use Principal Component Analysis(PCA) to perform linear transformations and dimensionality reduction.
Data structure: intrinsic dimensionality of the data and/or sparsity of the data.
These combine very well with techniques of unstructured and redundant dictionaries of functions and provide a fundamental approach to lower the dimensionality of complex problems.
The 1x1 convolutions(or network in network layer) provide a method of dimensionality reduction.
Because the number of such features is smaller than the number of pixels, this kind of pre-processing represents a form of dimensionality reduction.
Having three distinct zones in the image helps create a sense of depth, three dimensionality.
The gradations in colour add a dimensionality to the picture that shoves 3D to the back of our minds.
Self concept/academic achievement relations: An investigation of dimensionality, stability& causality.
Data structure: intrinsic dimensionality of the data and/or sparsity of the data.
But Lee eventually concluded that the richness and dimensionality of 3D would deepen the story and advance his unique vision.
The main purpose of PCA is to significantly reduce the dimensionality of the features that allows them to describe the“typical” features of different faces.
We might even be able to manipulate the dimensionality of space itself, creating bizarre artificial worlds with unimaginable properties.
True or False Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
You actually need to expand its dimensionality to meet the broadcasting rules above.
Self-concept/academic achievement relations: An investigation of dimensionality, stability, and causality.
Spatial Pooling(also called subsampling or downsampling) reduces the dimensionality of each feature map but retains the most important information.
So you have just reduced the dimensionality of“shop locations in Palo Alto” from two to one.
This shows that, implicit in the LDA classifier, there is a dimensionality reduction by linear projection onto a dimensional space.
When used to transform data, PCA can reduce the dimensionality of the data by projecting on a principal subspace.