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To initialize the process, first all the target domain data is projected into an m-dimensional subspace.
The data is projected onto directions that increase the signal-to-noise ratio (SNR) of the training data [17].
In higher dimensions, the data is projected onto a subspace; several components are missing, and several components are available.
4G LTE networks will help, but won't solve the worsening data crunch, as data is projected to grow over 18-fold, and 4G will only increase network capacity by around four-fold.
(2) Then, each pixel vector in the input data is projected onto the entire set of skewers, and the pixels falling at the extremes of each skewer are tallied (see Figure 2).
(4) For classification purposes, each test data is projected over the JLNMF PS-specific and shared TF bases obtained in the previous step, and the coefficient vector is computed using the updating rule in Eq. (3), and the distinct structures corresponding to the PS-specific TF bases are reconstructed for all the test data.
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Volumes of data are projected to reach 40ZB by 2020, or 5,247 GB per person, with emerging economies accounting for an increasingly large proportion of the world's total.
Next, the metabolome data was projected onto PCA.
The transformed data are projected on two new axes.
Then, data are projected into a lower dimensional subspace using orthogonal transformations.
To do this, the source classifier and the labeled target domain data are projected onto a one-dimensional space.
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