In this work, we develop inferential tools for determining the correct number of principal components under a general noisy latent variable model, which includes as a special case, for example, the noisy independent component model. The problem is approached using hypothesis testing, and we provide both a large‐sample test and several resampling‐based alternatives. Simulations and an application to sound data reveal that both types of approaches keep the desired levels and have good power.Peer reviewe
How do statistical dependencies in measurement noise influence high-dimensional inference? To answer...
Estimating the number of sources provides useful in-formation for signal processing applications, su...
In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser...
In this work, we develop inferential tools for determining the correct number of principal component...
Principal component analysis is one of the most commonly used multivariate tools to describe and sum...
Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as ...
The identification of a reduced dimensional representation of the data is among the main issues of e...
<p>Number of Principal Components (PC, mean ± SD) and Variance Ratio (VR) of PCA representations of ...
AbstractRobust principal component analysis (RPCA) is a powerful procedure which decomposes a matrix...
Recently, the technique of principal component analysis (PCA) has been expressed as the maximum like...
This paper demonstrates the effect of independent noise in principal components of k normally distri...
The research on robust principal component analysis (RPCA) has been attracting much atten-tion recen...
Independent Factor Analysis (IFA) has recently been proposed in the signal pro-cessing literature as...
We present in this paper a signal subspace-based approach for enhancing a noisy signal. This algorit...
Recently, the technique of principal component analysis (PCA) has been expressed as the maximum like...
How do statistical dependencies in measurement noise influence high-dimensional inference? To answer...
Estimating the number of sources provides useful in-formation for signal processing applications, su...
In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser...
In this work, we develop inferential tools for determining the correct number of principal component...
Principal component analysis is one of the most commonly used multivariate tools to describe and sum...
Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as ...
The identification of a reduced dimensional representation of the data is among the main issues of e...
<p>Number of Principal Components (PC, mean ± SD) and Variance Ratio (VR) of PCA representations of ...
AbstractRobust principal component analysis (RPCA) is a powerful procedure which decomposes a matrix...
Recently, the technique of principal component analysis (PCA) has been expressed as the maximum like...
This paper demonstrates the effect of independent noise in principal components of k normally distri...
The research on robust principal component analysis (RPCA) has been attracting much atten-tion recen...
Independent Factor Analysis (IFA) has recently been proposed in the signal pro-cessing literature as...
We present in this paper a signal subspace-based approach for enhancing a noisy signal. This algorit...
Recently, the technique of principal component analysis (PCA) has been expressed as the maximum like...
How do statistical dependencies in measurement noise influence high-dimensional inference? To answer...
Estimating the number of sources provides useful in-formation for signal processing applications, su...
In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser...