In this paper, we propose a unifying treatment of several strategies for training mixture models from label-deficient data. After a review of different approaches to estimating classification models on partially labelled data using mixture models, we identify a number of problems which lead us to propose a new EM variant. The aim is to better handle unlabelled data and provide a more confident ...

Imagine your local creamery administers a survey asking their patrons to choose their five favorite ice cream flavors. Any data collected by this survey would be an example of partially ranked data, as the set of all possible flavors is only ranked into subsets of the chosen flavors and the non-chosen flavors. If the creamery asks you to help analyze this data, what approaches could you take? O...

This paper presents a simplified version of Data Envelopment Analysis (DEA) a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object the one having greatest outputs and smal...

Statistical models on full and partial rankings of n items are often of limited practical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive efficient procedures for their use for large n. The derivations are largely possible through combinatorial and algebraic manipulations based on the lattice of partial ranking...

With the diverse new capabilities that sensor and ad-hoc networks can provide, applicability of data aggregation is growing. Data aggregation is useful in dealing with multi-value domain information, which often requires approximate agreement decisions among nodes. In contrast to fully connected networks, the research on data aggregation for partially connected networks is very limited. This is...

A central task when integrating data from different sources is to detect identical items. For example, price comparison websites have to identify offers for identical products. This task is known, among others, as record linkage, object identification, or duplicate detection. In this work, we examine problem settings where some relations between items are given in advance – for example by EAN a...

the size of each subset. However, in case the missing ness not completely at random (MCAR), Looney and Jones [9] argued that ignoring some of the correlated observations would bias the estimation of the variance of the difference in treatment means and would dramatically affect the performance of the statistical test in terms of controlling type I error rates and statistical power [10]. They pr...

Article history: Available online 16 June 2017

Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also en...

Often, in cross-sectional-follow-up studies, survival data are obtained from prevalent cases only. This sampling mechanism introduces lenght-bias. An added difficulty is that in some cases the times of the onset cannot be ascertained or are recorded with great uncertainty. Such was the situation in the Canadian Study of Health and Aging (CSHA), an ongoing nation-wide study of dementia conducted...