Yasutoshi Ida

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Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This(More)
The lasso-based L1-graph is used in many applications since it can effectively model a set of data points as a graph. The lasso is a popular regression approach and the L1-graph represents data points as nodes by using the regression result. More specifically, by solving the L1-optimization problem of the lasso, the sparse regression coefficients are used(More)
Stress-induced psychological and somatic diseases are virtually endemic nowadays. Written self-report anxiety measures are available; however, these indices tend to be time consuming to acquire. For medical patients, completing written reports can be burdensome if they are weak, in pain, or in acute anxiety states. Consequently, simple and fast non-invasive(More)
We propose a domain-dependent/independent topic switching model based on <i>Bayesian probabilistic modeling</i> for modeling online product reviews that are accompanied with numerical ratings provided by users. In this model, each word is allocated to a domain-dependent topic or a domain-independent topic, and the distribution of topics in an online review(More)
The design of master manipulators for master-slave surgical robotic systems is important because it may influence slave manipulator performance as well as the operator's workload. However, no design strategy has been presented thus far for optimizing the master manipulator design parameters. A master manipulator prototype and an experimental setup were(More)
A continuous-valued infinite relational model is proposed as a solution to the co-clustering problem which arises in matrix data or tensor data calculations. The model is a probabilistic model utilizing the framework of Bayesian Nonparametrics which can estimate the number of components in posterior distributions. The original Infinite Relational Model(More)
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approximate Hessianbased preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approximation may be inaccurate. In this paper,(More)
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