We develop the Kernel Multitask Latent Analysis (KMLA) method for modeling many-to-many relationships between inputs and responses, and show how it can be applied to inductive transfer problems in bioseparations. KMLA performs dimensionality reduction targeted towards a multitask loss function much like Kernel Partial Least Squares (KPLS). KPLS is limited to least squares multiple regression wh...

The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model selection or confidence intervals for more detailed analysis requires cubic runtime, and thus constitutes a computational bottleneck in real-world data analysis. We propose a novel algorithm for KP...

The authenticity of milk and milk products is important and has extended health, cultural, and financial implications. Current analytical methods for the detection of milk adulteration are slow, laborious, and therefore impractical for use in routine milk screening by the dairy industry. Fourier transform infrared (FT-IR) spectroscopy is a rapid biochemical fingerprinting technique that could b...

Genetic algorithm and partial least square (GA-PLS), the kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlationbetween retention time (RT) and descriptors for 15 nanoparticle compounds which obtained by thecomprehensive two dimensional gas chromatography system (GC x GC). Application of thedodecanethiol monolayer-protect...

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as feedstock for ethylene cracker while minimizing processing time, is important to improve optimization production. In this study, a novel approach estimating HBO samples was developed basis near-infrared (NIR) spectra. The main noise and extreme in spectral data were removed by combining discrete wave...

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M . Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. ...

a r t i c l e i n f o In this paper, we propose a learning-based super resolution approach consisting of two steps. The first step uses the kernel partial least squares (KPLS) method to implement the regression between the low-resolution (LR) and high-resolution (HR) images in the training set. With the built KPLS regression model, a primitive super-resolved image can be obtained. However, this...

In a wind tunnel process, Mach number is the most important parameter. However, it difficult to measure directly, especially in multimode operation leading difficulty process monitoring. Thus, necessary indirectly by utilizing data-driven methods, and based on which, monitor status of process. this paper, therefore, flow field system monitoring strategy proposed. Since strongly nonlinear system...

Selecting the process variables is an important prerequisite for establishing an accurate model of aluminum electrolytic process. A variable selection method is researched and proposed based on the False Nearest Neighbors (FNN) and Randomization Method (RM) (FR) in KPLS(Kernel Partial Least Squares) feature space. Firstly, the KPLS is employed to transform the original space to the PLS feature ...