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The subnets are based on some well-trained auto-associative neural networks (AANNs), which can reduce dimension and filter out noise.
To reduce the computational cost and maintain high accuracy, closed-form solutions are utilized for evaluating the response variance and modal truncation is adopted to reduce dimension (the effect of which is investigated).
As stated previously, PCA is able to reduce dimension and to decorrelate the components.
Wavelet coefficients of EEG signals were assumed as feature vectors and statistical features were used to reduce dimension of those feature vectors.
Specifically, we first apply principal component analysis (PCA) to an unfolded feature vector of GEI to reduce dimension and subsequently apply LDA to get discriminant features.
In addition, to reduce dimension and avoid overfitting, CNN adopts a pooling operation to realize aggregated sampling for the previous layer network features.
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A stepped-impedance structure is integrated into this new filter simultaneously to reduce dimensions and suppress the spurious responses.
In the year 1987, Sirovich and Kirby used PCA to reduce dimensions for features vector of faces [26].
Using Deep Learning algorithms can help us to extract semantic features from a massive amount of text data in addition to reduce dimensions of the data representations.
Thus, SOMs accomplish two goals: they reduce dimensions and display similarities.
Soneson et al. [ 292] applied PCA to reduce dimensions, and Canonical Correlation Analysis (CCA) to identify highly correlated CNA/GE pairs.
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