Concept

Missing data

Related publications (75)

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

Are Spatial and Global Constraints Really Necessary for Segmentation?

Pascal Fua, Kevin Smith, Aurélien Lucchi, Yunpeng Li, Xavier Boix Bosch

Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can gr ...

IEEE2011

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

Boosting Gossip for Live Streaming

Rachid Guerraoui, Anne-Marie Kermarrec, Maxime Monod, Davide Frey

Gossip protocols are considered very effective to disseminate information in a large scale dynamic distributed system. Their inherent simplicity makes them easy to implement and deploy. However, whereas their probabilistic guarantees are often enough to di ...

2010

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

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

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 with missing data via nuclear norm regularization

Colin Neil Jones

2009

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Daniel Gatica-Perez, Katayoun Farrahi

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, ...

2009

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