Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems

The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm

The Diagnosis and Treatment of Early-Stage Colorectal Cancer

The introduction of colorectal endoscopic submucosal dissection (ESD) has expanded the applications for endoscopic treatment; as a result, lesions with low metastatic potential can be treated endoscopically regardless of the lesion size. The most attractive feature of ESD is the achievement of en bloc resection with a lower local recurrence rate in comparison to that of endoscopic piecemeal mucosal resection. However, in case of gastric cancers, ESD is not as widely applied to the treatment of colorectal neoplasms because of its technical difficulty, longer procedural time, and increased perforation risk. In the movement toward diversified endoscopic treatment strategies for superficial colorectal neoplasms, endoscopists who begin to perform ESD need to recognize the indications of ESD, as well as the technical issues and associated complications of this procedure

Fast Data-driven Greedy Sensor Selection for Ridge Regression

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator which is trained based on the data. The proposed algorithm greedily selects sensors for minimization of the cost function of the estimator. Sensor selection which prevents the overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. Furthermore, we show that sensor selection can be accelerated by dimensionality reduction of the target variables without large deterioration of the estimation performance. The effectiveness of the proposed algorithm is verified for two real-world datasets. The first dataset is a dataset of sea surface temperature for sensor selection for reconstructing large data, and the second is a dataset of surface pressure distribution and yaw angle of a ground vehicle for sensor selection for estimation. The experiments reveal that the proposed algorithm outperforms some data-drive selection algorithms including the orthogonal matching pursuit

レンブラント作《アブラハムの犠牲》に見るモティーフの後退と逸失

（付記）本稿は、平成22年10月に行われた筑波大学芸術学美術史学会秋季研究発表会での口頭発表に基づき、大幅な加筆訂正を行ったものです。著作権保護のため、すべての掲載図版に墨消し処理を施しています

Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets