Solving Least Squares Problems.

  title={Solving Least Squares Problems.},
  author={Robert F. Ling and Charles L. Lawson and Richard J. Hanson},
  journal={Journal of the American Statistical Association},
Bayesian matrix factorisation : inference, priors, and data integration
This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about the physical properties of E-modulus and its properties as well as provide a probabilistic guide to its application in the classroom.
Essays on hyperspectral image analysis : classification and target detection
Five new methods to tackle the issues of HSI classification and HSI target detection are proposed and include the dictionary learning, which incorporates the JSM in the discriminative K-SVD learning algorithm in order to learn a quality dictionary with rich information for improving the classification performance.
N ov 2 01 7 Oracle inequalities for sign constrained generalized linear models
The theoretical properties of signconstrained generalized linear models with convex loss function are studied, which is one of the sparse regression methods without tuning parameters, which encompasses the logistic and quantile regressions.
Theory , validation and application of blind source separation to diffusion MRI for tissue characterisation and partial volume correction
Here we present blind source separation (BSS) as a new tool to analyse multi-echo diffusion data. This technique is designed to separate mixed signals and is widely used in audio and image
Scalable Super Learning
Feedback Regulation of Human Hematopoietic Stem Cell Fate
Identification of breed contributions in crossbred dogs
Experimental results on a synthetic, admixed test dataset using AIMs showed that the MCMC approach successfully predicts breed proportions for a variety of lineage complexities and the HMM approach performed less well, presumably due to using less information of the dataset.
Robustness Assessment of 1-D Electron Paramagnetic Resonance for Improved Magnetic Nanoparticle Reconstructions
This paper improves the solution of the inverse problem by introducing a combination of truncated singular value decomposition and nonnegative least squares, which enables to recover both smooth and discontinuous MNP distributions.
Fast Numerical and Machine Learning Algorithms for Spatial Audio Reproduction
This dissertation develops several numerical and machine learning algorithms for accelerating and personalizing spatial audio reproduction in light of available mobile computing power and introduces a novel sparse decomposition algorithm for HRIRs based on non-negative matrix factorization that allows for faster time-domain convolution than frequency-domain fastFourier-transform variants.
Climate Multi-model Regression Using Spatial Smoothing
This paper addresses the problem of combining multiple GCM model outputs with spatial smoothing as an important desired criterion, and establishes the superiority of the approach in terms of model accuracy and smoothing compared to several popular baselines on real GCM climate datasets.