Tomoya Sakai

Convex formulation of multiple instance learning from positive and unlabeled bags

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available. MIL has a variety of applications such as content-based image retrieval, text categorization, and medical diagnosis. Most of the previous work for MIL assume that training bags are fully labeled. However, it is often difficult to obtain an enough number of labeled bags in practical situations, while many unlabeled bags are available. A learning framework called PU classification (positive and unlabeled classification) can address this problem. In this paper, we propose a convex PU classification method to solve an MIL problem. We experimentally show that the proposed method achieves better performance with significantly lower computation costs than an existing method for PU-MIL.

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning

On page 7, 4.2 Variance reduction, the third line of the equation needs to be corrected as follows:

Semi-supervised AUC optimization based on positive-unlabeled learning

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.

Design and development of miniature parallel robot for eye surgery

A five degree-of-freedom (DOF) miniature parallel robot has been developed to precisely and safely remove the thin internal limiting membrane in the eye ground during vitreoretinal surgery. A simulator has been developed to determine the design parameters of this robot. The developed robots size is 85 mm × 100 mm × 240 mm, and its weight is 770 g. This robot incorporates an emergency instrument retraction function to quickly remove the instrument from the eye in case of sudden intraoperative complications such as bleeding. Experiments were conducted to evaluate the robots performance in the master-slave configuration, and the results demonstrated that it had a tracing accuracy of 40.0 μm.