  2D Convolutional neural network (CNN) has arguably become the de facto
standard for computer vision tasks. Recent findings, however, suggest that CNN
may not be the best option for 1D pattern recognition, especially for datasets
with over 1 M training samples, e.g., existing CNN-based methods for 1D signals
are highly reliant on human pre-processing. Common practices include utilizing
discrete Fourier transform (DFT) to reconstruct 1D signal into 2D array. To add
to extant knowledge, in this paper, a novel 1D data processing algorithm is
proposed for 1D big data analysis through learning a deep
deconvolutional-convolutional network. Rather than resorting to human-based
techniques, we employed deconvolution layers to convert 1 D signals into 2D
data. On top of the deconvolution model, the data was identified by a 2D CNN.
Compared with the existing 1D signal processing algorithms, DCNet boasts the
advantages of less human-made inference and higher generalization performance.
Our experimental results from a varying number of training patterns (50 K to 11
M) from classification and regression demonstrate the desirability of our new
approach.
