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We can use the largest N eigenvalues and their respective eigenvectors of (hat{mathbf{H}}) to obtain the projection of individual linear filter components (widehat{mathcal{P}_{1}g^{n}_{1}}), (n=1,ldots,N).
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The linear filter is often associated with the neuron's receptive field.
A truly linear filter does not cause harmonic or intermodulation distortion.
Our method has been used on grayscale, RGB, HSV, and local linear filter responses.
Thus, a causal linear filter matrix will have entries that satisfy for.
A starting point for many such analyses has been provided by phenomenological "linear nonlinear" (LN) models, which comprise a linear filter followed by a static nonlinear transformation.
The Kalman Filter was designed as a linear filter.
The linear filter is given by (24).
individual effects and individual linear trends (denoted 3).
For each layer the linear filter is recalculated.
Obviously, the linear filter performs worst for all setups.
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