The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and ...

The Active Appearance Model (AAM) is a widely used approach for model based vision showing excellent results. But one major drawback is that the method is not robust against occlusions. Thus, if parts of the image are occluded the method converges to local minima and the obtained results are unreliable. To overcome this problem we propose a robust AAM fitting strategy. The main idea is to apply...

Algorithm 1 Suggesting Compatible Colors 1: procedure COMPATIBLECOLORS(palette t, index k, #cands Ncand, #samples Nsample, threshold (τ, κ)) 2: . Sampling candidate’s HSVs 3: f ← COMPUTEHUEPROBABILITY(t, k) . Eq. 3 or Eq. 4 4: hi← SAMPLINGFROMHUEPROB( f , Nsample) 5: si ∼N (μs,σs) . §4.2 6: vi ∼N (μv,σv) . §4.2 7: . Compute rating 8: for i = 1→ m do 9: ci← (hi, si,vi) 10: Ccand i ← COMPATIBLECA...

Mahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing rerandomization strategy. However, this may not work well for high-dimensional cases because it balances all orthogonalized covariates equally. We propose using principal component analysis (PCA) to identify proper subspaces in which should be calculated. Not on...

Wavetable matching of musical instrument tones using principal components analysis (PCA) takes advantage of spectral correlation information to find the basis spectra. Although PCA matching is efficient, it usually poorly matches the low-amplitude parts of a tone due to its inherent statistical bias. This paper describes weighted PCA methods that normalize the tone before PCA to fairly weight i...

This paper describes the application of Principal Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. PCA is a linear dimensionality reduction technique. In order to diagnosis the faults, the PCA approach includes one PCA model for each system behavior, i.e., a PCA model for normal operation conditions and a PCA model for each faulty situation. Data set is generate...

a novel fast image fusion scheme based on principal component analysis (pca) and lifting wavelet transformation (LWT) is proposed. Firstly, the principal component images of the registered original colour image are obtained by pca transformation. Then, the first principal component image and near infrared imagery are merged using lifting wavelet transformation (LWT) based on regional features. ...

This paper presents a generalization of the Principal Component Analysis (PCA) demodulation method. The accuracy of the traditional method is limited by the number of fringes in the interferograms and it cannot be used when there are one or less interferometric fringes. The Advanced Iterative Algorithm (AIA) is robust in this case, but it suffers when the modulation and/or the background illumi...