In recent years, rapid digitization and technological advances have enabled governments and companies to utilize various types of personal information, including environment, medical, and lifestyle. Privacy protection is an important issue in the utilization of personal information, and anonymization methods such as k-anonymization and 1-diversity have been proposed in the past. However, to utilize data effectively, it is also important not to lose the usefulness of the information. Privacy protection and preserving the usefulness of information are in a trade-off relationship, and balancing these two aspects has been a difficult problem. Recently, microaggregation using clustering algorithms has been attracting attention as an anonymization method that ensures the usefulness of the data while protecting privacy. In this study, we proposed a new microaggregation method based on iterative processing using probabilistic latent semantic analysis and evaluated the proposed method based on the use case. The use case is a classification of social isolation and an analysis of the relationship with physical frailty risk using the classified groups. Compared to microaggregation using quasi-identifiers and microaggregation based on a conventional clustering algorithm, the proposed method was able to find social isolation characteristics obtained in the raw data with higher accuracy. In addition, in a more detailed analysis focusing on a population with certain characteristics, the proposed method was able to replicate the analysis results obtained using the raw data and to make appropriate assessments of the health risks.

This paper analyzes a questionnaire survey data on elderly people in order to investigate regional characteristics of their living activities. For the purpose, we use Probabilistic Latent Spatial Semantic (PLSS) Modeling, which is integrated the two methods: probabilistic latent semantic analysis (pLSA) and Bayesian network (BN). First, we aggregate each individual's survey record by postal code; Second, we find characteristics of the region by pLSA; Third, we use BN to clarify factors of this regional disparity. From the study, we are able to identify critical information to support decisions for a manager in a local government: i) The responses of the elderly are correlated with their resident areas; and ii) The regional disparity of social network will improve by neighborhood facilities. Such information will be of use for designing the super-aged society in the near future.

Many injures during childhood are related to the use of playground equipment. Until recently, scientific data of how children actually use playground equipment were scarce. Childhood injury cases were not examined thoroughly from the perspective of how equipment can be modified for improving safety without ruining its attraction to children. To design age-appropriate and safer playground equipment, it is essential that scientific data on the interaction between children and this equipment be accumulated. Herein we report on studies to develop new playground equipment by applying sensor technology to examine the science behind children's interaction with playground equipment. We developed a rock-climbing wall equipped with force sensors to record the physical behavior of children while on the wall, thus allowing measurement of these behaviors in a more natural environment. Fifty force sensors installed in the developed rock-climbing wall are able to collect a large amount of data while children are playing with the equipment. The behavior data of 623 children were recorded in the present study. Herein, we also report on a child behavior prediction model created from the collected data.

In the framework of personalization or micromarketing of services, an effective strategy is to examine customers or items of a specific category. This paper describes an actual service support system using discovery of category-based customer behavior knowledge. The method is realized by modeling a customers' purchase behavior with some purchase situations or conditions using massive point of sales data with a customer ID (ID-POS data) in a department store chain. We automatically generate categories of customers and items based on a purchase patterns identified in ID-POS data using probabilistic latent semantics indexing. We produce a Bayesian network model including the customer and item categories, situations and conditions of purchases, and the properties and demographic information of customers. Based on that network structure, we can systematically identify useful knowledge for use in furthering business intelligence or sustainable services. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.

This paper describes an appropriate category discovery method that simultaneously involves a customer's lifestyle category and item category for the sustainable management of retail services, designated as ``category mining''. Category mining is realized using a large-scale ID-POS data and customer's questionnaire responses with respect to their lifestyle. For the heterogeneous data fusion, we propose a probabilistic double-latent semantic indexing (PdLSI) model that is an extension of PLSI model. In the PdLSI model, customers and items are classified probabilistically into some latent lifestyle categories and latent item category. Then, understanding of relation between the latent categories and various purchased situations is realized using Bayesian network modeling. This method provides useful knowledge based on a large-scale data for efficient customer relationship management and category management, and can be applicable for other service industries.

By understanding the behavior, satisfaction level, or values of the customer, the productivity and level of customer satisfaction of a service industry can be improved. Such customer-based considerations are estimated from questionnaire data in a general manner. The useful estimation of such considerations requires effective methods for modeling the cognitive structures of customers based on such data. However, it is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. The present paper describes a method of constructing a probabilistic model of the cognitive structure of the customer, which clarifies the satisfaction level and decision making process of the customer of a retail service through statistical graphical modeling. The proposed method constructs a probabilistic cognitive structure model by integrating questionnaire data and a Bayesian network, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The model structure can be constructed automatically based on information criteria and can embed some of the experiences of the model designer and/or physical or social rules in advance. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. We obtained useful knowledge for function design and marketing from the model constructed by a simulation and sensitivity analysis. The proposed method can be applied to various services that use a variety of data.

High-dimensional data generated by a system with limited degrees of freedom are often constrained in low-dimensional manifolds in the original space. In this article, we investigate dimension-reduction methods for such intrinsically low-dimensional data through linear projections that preserve the manifold structure of the data. For intrinsically one dimensional data, this implies projecting to a curve on the plane with as few intersections as possible. We are proposing a supervised projection pursuit method that can be regarded as an extension of the single-index model for nonparametric regression. We show results from a toy and two robotic applications.

We propose a method for extracting low-dimensional features from omnidirectional images to be used for robot localization and navigation. Edge detection is combined with thresholding to locate sharp edge pixels, the coordinates of which are fed into a Parzen density estimator (1962) to compute the edge spatial density. The use of the fast Fourier transform makes this density estimate feasible in real-time, while principal component analysis further drops the dimensionality of the resulting feature vector to a manageable number. We show experimental results from a Nomad XR4000 robot in an office environment.

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "true" score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function.

We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk function to minimize is the conditional entropy of the robot positions given the projected observations. We propose a method of iterative optimization through a probabilistic model based on kernel smoothing. To obtain good starting optimization solutions we use canonical correlation analysis. We apply our method on a real experiment involving a mobile robot equipped with an omnidirectional camera in an office setup.

For information retrieval (IR) tasks, user models are used to estimate user's true intention and demand. Unfortunately, most user models are constructed in a specialized form that is not applied to other systems or domains. This specialization makes it difficult to share user models as common resources for developing information retrieval systems and for researching cognitive characteristics in various users. In order to solve this problem, we need a general user modeling method. A user model based on a probabilistic framework is proposed. We call this model a generative user model. The generative user model represents user's mental depth by latent (hidden) variables. It also has visible variables that mean word set and qualifier of each word as a subjective probability distribution. The model can handle uncertainty of the user's subjectivity by a probabilistic framework. Recent statistical studies for such latent models give a learning algorithm. Our generative user model can be constructed from a dataset taken by information retrieval tasks. As an example, we also introduce two different kinds of information retrieval systems, ART MUSEUM (Multimedia Database with Sense of Color and Construction upon the Matter of ART) and DSIU (Decision Support for Internet Users). The generative user model is applied to these systems. The properties of the model and interactive learning mechanism are shown.

A design of mobile robot for robust life-long navigation in office environment is proposed and evaluated. The key idea is combining probabilistic map and dialog with humans for reducing the location uncertainty. Bayesian inference with the map represented by probabilistic automata is used in order to reduce the number of queries and to evaluate the success rate of planned paths. We experimentally implemented the design using a simple Bayesian network with continuous nodes and demonstrated its effectiveness in a real environment.