Multivariate denoising using wavelets and principal component analysis

By Mina Aminghafari, Nathalie Cheze, Jean‐Michel Poggi. 2006

206 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jacinta Chan Phooi M’ng
2. Sarath Kodagoda
3. Rami N. Khushaba
4. M. Mehralizadeh

In Control and Systems Engineering

1. Jacinta Chan Phooi M’ng
2. M. Mehralizadeh

In Signal Processing

1. Jacinta Chan Phooi M’ng
2. Sarath Kodagoda
3. Rami N. Khushaba
4. M. Mehralizadeh

Citations in the corpus

• Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models (2016, methodology): “Denoising algorithms based onWavelet Transform have become a widespread technique for single-dimensional signal filtering and data mining [28, 32, 33].”
• Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking (2013, methodology): “After the application of ICA, the resulting bsICAðtÞ (ICA’s version of s(t)) are usually manually inspected to identify the independent components corresponding to artifacts, where such components are replaced by zeros to construct a new ICA data (Akhtar et al., 2012; Aminghafari et al., 2006; Caste”