Simultaneous Bayesian Clustering and Feature Selection Through Student's ${t}$ Mixtures Model.

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student's distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features' salience are included in the model for the indication of the relevance of features.

Canonical correlation analysis on data with censoring and error information.

We developed a probabilistic model for canonical correlation analysis in the case when the associated datasets are incomplete. This case can arise where data entries either contain measurement errors or are censored (i.e.

Individual responses to a method of cursor assistance.

Purpose: The aim of this study was to evaluate a new click assistance technique, Steady Clicks, designed to help computer users with motor impairments to click more accurately using a mouse. Specifically, Steady Clicks suppresses two types of errors: slipping while clicking and accidentally clicking. Steady Clicks suppresses these errors by freezing the cursor during mouse clicks, preventing overlapping button presses and suppressing clicks made while the mouse is moving at a high velocity.