Though existing state-of-the-art denoising algorithms, such as BM3D, LPG-PCA and DDF, obtain remarkable results, these methods are not good at preserving details at high noise levels, sometimes even introducing non-existent artifacts. To improve the performance of these denoising methods at high noise levels, a generic denoising framework is proposed in this paper, which is based on guided prin...

A novel text data dimension reduction technique, called the tree-structured multi-linear principle component analysis (TMPCA), is proposed in this work. Being different from traditional text dimension reduction methods that deal with the word-level representation, the TMPCA technique reduces the dimension of input sequences and sentences to simplify the following text classification tasks. It i...

Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a “black box” operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that...

Principle Component Analysis (PCA) is used to reduce dimensionality and noise, while still preserving the majority of the variance in the data. It however gives little guarantee on the predictive value of the remaining data. This paper proposes an inverted Principle Component Analysis (iPCA), to achieve dimensionality and noise reduction, by removal of the largest principle components. In addit...

Abstract This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate regionalization for pavement infrastructure. The effect significance of climate change were firstly evaluated using time as only predictor t-test. It was found that both temperature ...

in the present study, 45 rainwater samples were collected from february to december 2012 on event basis in east bokaro coal mining environment. physico-chemical and major ionic compositions of rainwater samples as well as water soluble major ion composition were analyzed to employ principle component analysis for source identification. the average ph value was recorded 6.1 and varied from 5.1 t...

We propose the kernel-based nonlinear independent component analysis (ICA) method, which consists of two separate steps. First, we map the data to a high-dimensional feature space and perform dimension reduction to extract the effective subspace, which was achieved by kernel principal component analysis (PCA) and can be considered as a pre-processing step. Second, we need to adjust a linear tra...

Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a “black box” operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that...