Mutual information for the selection of relevant variables in spectrometric nonlinear modelling

Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of parameters, leading to overfitting and poor generalization abilities. In this paper, we suggest the use of the mutual information measure to select variables from the initial set. The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is thus suitable for nonlinear modelling. In addition, it leads to the selection of variables among the initial set, and not to linear or nonlinear combinations of them. Without decreasing the model performances compared to other variable projection methods, it allows therefore a greater interpretability of the results

Transcriptomic Analysis of Human Retinal Detachment Reveals Both Inflammatory Response and Photoreceptor Death

Background Retinal detachment often leads to a severe and permanent loss of vision and its therapeutic management remains to this day exclusively surgical. We have used surgical specimens to perform a differential analysis of the transcriptome of human retinal tissues following detachment in order to identify new potential pharmacological targets that could be used in combination with surgery to further improve final outcome. Methodology/Principal Findings Statistical analysis reveals major involvement of the immune response in the disease. Interestingly, using a novel approach relying on coordinated expression, the interindividual variation was monitored to unravel a second crucial aspect of the pathological process: the death of photoreceptor cells. Within the genes identified, the expression of the major histocompatibility complex I gene HLA-C enables diagnosis of the disease, while PKD2L1 and SLCO4A1 -which are both down-regulated- act synergistically to provide an estimate of the duration of the retinal detachment process. Our analysis thus reveals the two complementary cellular and molecular aspects linked to retinal detachment: an immune response and the degeneration of photoreceptor cells. We also reveal that the human specimens have a higher clinical value as compared to artificial models that point to IL6 and oxidative stress, not implicated in the surgical specimens studied here. Conclusions/Significance This systematic analysis confirmed the occurrence of both neurodegeneration and inflammation during retinal detachment, and further identifies precisely the modification of expression of the different genes implicated in these two phenomena. Our data henceforth give a new insight into the disease process and provide a rationale for therapeutic strategies aimed at limiting inflammation and photoreceptor damage associated with retinal detachment and, in turn, improving visual prognosis after retinal surgery

Statistical fault isolation with PCA

The paper considers a novel approach to isolating instrumentation faults. The key ideas involved are the existing connections between PCA and parity equations, the extension of PCA-like techniques to nonlinear cases and, finally, the use of the parametric statistical approach as a framework to detect and isolate faults. The methodology presented is based on a combination of PCA-based modeling techniques with the parametric statistical approach for residual generation and evaluation.Anglai

Bootstrap for Model Selection: Linear Approximation of the Optimism

of the optimis

An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset: Application to Tanganyika Lake Dataset

In this paper, an improved methodology for the determination of missing values in a spatio-temporal database is presented. This methodology performs denoising projection in order to accurately fill the missing values in the database. The improved methodology is called EOF Pruning and it is based on an original linear projection method called Empirical Orthogo- nal Functions (EOF). The experiments demonstrate the performance of the improved methodology and present a comparison with the original EOF and with a widely-used Optimal Interpolation method called Objective Analysis.CLIMFIS

Vector Quantization: A Weighted Version For Time-Series Forecasting

Nonlinear time-series prediction offers potential performance increases compared to linear models. Nevertheless, the enhanced complexity and computation time often prohibits an efficient use of nonlinear tools. In this paper, we present a simple nonlinear procedure for time-series forecasting, based on the use of vector quantization techniques; the values to predict are considered as missing data, and the vector quantization methods are shown to be compatible with such missing data. This method offers an alternative to more complex prediction tools, while maintaining reasonable complexity and computation time

Nonlinear time series prediction by weighted vector quantization

Abstract. Classical nonlinear models for time series prediction exhibit improved capabilities compared to linear ones. Nonlinear regression has however drawbacks, such as overfitting and local minima problems, user-adjusted parameters, higher computation times, etc. There is thus a need for simple nonlinear models with a restricted number of learning parameters, high performances and reasonable complexity. In this paper, we present a method for nonlinear forecasting based on the quantization of vectors concatenating inputs (regressors) and outputs (predictions). Weighting techniques are applied to give more importance to inputs and outputs respectively. The method is illustrated on standard time series prediction benchmarks.