Linear and logistic regression are well-known data mining techniques, however, their ability to deal with interdependent variables is limited. Principal component analysis (PCA) is a prevalent data reduction tool that both transforms the data orthogonally and reduces its dimensionality. In this paper we explore an adaptive hybrid approach where PCA can be used in conjunction with logistic regre...

We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.

Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure...

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. ...

Background: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.Material and Meth...

Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM) might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods for EEG pattern classification which jointly employ principal component anal...

In view of two fundamental reasons, face recognition system has gotten consideration in the research. The primary reason is the extensive variety of business and law enforcement applications and second reason is concerned with the accessibility of the attainable advancements. The combination of PCA-SVM (Principal Component Analysis and Support Vector Machine) and PCA-ANN (Principal Component an...

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parame...

Dual Bounds of Sparse Principal Component Analysis principal component analysis (PCA) is a widely used dimensionality reduction tool in machine learning and statistics. Compared with PCA, sparse PCA enhances the interpretability by incorporating sparsity constraint. However, unlike conventional heuristics for cannot guarantee qualities obtained primal feasible solutions via associated dual boun...