Noise robustness is the most important issue for real-life speech recognition/spoken dialogue system. In this paper, a sub-space missing feature theory (S-MFT) front-end is proposed to alleviate the corruption of the background noise. SMFT incorporates temporal information and applies principle component analysis (PCA) to find a noise-suppressed subspace for more precise missing feature imputat...

We consider a stage life testing model and assume that the information at which levels the failures occurred is not available. In order to find estimates for the lifetime distribution parameters, we propose an EM-algorithm approach which interprets the lack of knowledge about the stages as missing information. Furthermore, we illustrate the implementation difficulties caused by an increasing nu...

Deciding the outcome of an election when voters have provided only partial orderings over their preferences requires voting rules that accommodate missing data. While existing techniques, including considerable recent work, address missingness through circumvention, we propose the novel application of conventional machine learning techniques to predict the missing components of ballots via late...

In this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multi-camera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a temporal sequence of independently reconstructed surfaces and iteratively deforms a reference mesh to fit ...

Min Deng 1, Zide Fan 1, Qiliang Liu 1,* and Jianya Gong 2 1 Department of Geo-Informatics, Central South University, Changsha 410083, China; [email protected] (M.D.); [email protected] (Z.F.) 2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [email protected] * Correspondence: [email protected]; Tel.: +86-137-8...

In this paper, we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. We defined a general binary relation on the original incomplete data which decomposed into data subsets without missing values. Next, topological base relation method for classifier induction is applied to such sets. New approach to find the missing values in consi...

Unfolding creates configurations from preference information. In this paper, it is argued that not all preference information needs to be collected and that good solutions are still obtained, even when more than half of the data is missing. Simulation studies are conducted to compare missing data treatments, sources of missing data, and designs for the specification of missing data. Guidelines ...