Concept

Imputation (statistique)

Publications associées (53)

Spectral modeling of time series with missing data

Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the for ...

Elsevier2013

Diffusion estimation over cooperative networks with missing data

Ali H. Sayed

In many fields, and especially in the medical and social sciences and in various recommender systems, data are often gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they ...

IEEE2013

Sparse Iterative Closest Point

Mark Pauly, Sofien Bouaziz, Andrea Tagliasacchi

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. Thes ...

Wiley-Blackwell2013

Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes

Jian Wang, Jun Wang, Lei Huang, Xun Xu, Jeffrey David Jensen, Xin Liu, Wen Wang, Chang Yu

Rice is a staple crop that has undergone substantial phenotypic and physiological changes during domestication. Here we resequenced the genomes of 40 cultivated accessions selected from the major groups of rice and 10 accessions of their wild progenitors ( ...

Nature Publishing Group2012

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

2010

Methodologies for model-free data interpretation of civil engineering structures

Ian Smith, Daniele Posenato

Structural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation This paper presents two methodologies for model-free data in ...

Elsevier2010

Robust activity recognition for assistive technologies: Benchmarking machine learning techniques

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha, Hamidreza Bayati, Daniel Roggen

An increasing need for healthcare provision and assistive technologies (AT) calls for the development of machine learning techniques able to cope with the variability inherent to real-world deployments. In the particular case of activity recognition applic ...

2010

Pay no attention to the alternatives behind the curtain: demand forecasting with missing data

Jeffrey Newman

Discrete choice modeling advances abound for scenarios where we can collect data about the behaviors of individuals, and observe their ultimate choice from among the range of possible alternatives. However, particularly in competitive private sector enterp ...

2009

System identification via nuclear norm regularization for simulated moving bed processes from incomplete data sets

Colin Neil Jones

The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of simulated moving bed proc ...

IEEE2009

System identification with missing data via nuclear norm regularization

Colin Neil Jones

2009

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