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Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for stat ...
With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling cha ...
Prediction in environmental systems, such as hydrological streamflow prediction, is a challenging task. Although on a small scale, many of the physical processes are well described, accurate predictions of macroscopical (e.g. catchment scale) behavior with ...
In this paper, a method for semi-supervised multiview feature extraction based on the multiset regularized kernel canonical correlation analysis (kCCA) is proposed for the classification of hyperspectral images. The covariance matrix of this type of data i ...
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...
Nonlocal means (NLM) is an effective denoising method that applies adaptive averaging based on similarity between neighborhoods in the image. An attractive way to both improve and speed-up NLM is by first performing a linear projection of the neighborhood. ...
Spectral reflection prediction models, although effective, are impractical for certain industrial applications such as self-calibrating devices and online monitoring because of the requirements imposed by their calibration. The idea emerged to make the cal ...
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold lear ...
The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...
When inferring models from hydrological data or calibrating hydrological models, we are interested in the information content of those data to quantify how much can potentially be learned from them. In this work we take a perspective from (algorithmic) inf ...
