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At the second layer of our feature hierarchy we apply Non-Negative Sparse Coding (NNSC) to obtain features spanning a larger frequency and time region.
The algorithm factorizes each network to obtain features using graph communicability, and then collapses the feature matrices to predict temporal links.
To overcome such difficulties, we constructed a tokenizer with white space and the symbols listed in Table 1 to segment the sentences into tokens, so that we could obtain features, like prefix, suffix, digit, specific symbols and so on.
To investigate the effect of different noise levels both the model-based and projection-based algorithms were used to obtain features from sensor responses with a 30-dB SNR. Figure 10 shows a typical reflectance sample projected onto the feature space when the SNR of both of the responses are 30 and 40 dB.
We use a wavelet transform method to obtain features.
To obtain features per experiment (cell population), averages of per-cell features were computed separately for mitotic and non-mitotic cells.
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Therefore, theta band dynamics were used in order to obtain feature vectors.
Both information is used to obtain feature vectors which represent a driving session.
We scale these values to obtain feature responses in the interval [0,1].
First, an ICA is run on the image series to obtain feature images – the ICs – and a feature mixing matrix.
The composition process stops when the obtained features satisfy the initial user requirement.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com