Population Synthesis via k-Nearest Neighbor Crossover Kernel

The recent development of multi-agent simulations brings about a need for population synthesis. It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin. This paper presents a new kernel density estimator for this task. Our method is an analogue of the classical Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the huge degree of freedom which is required to model high-dimensional nonlinearly correlated datasets: the crossover kernel, the k-nearest neighbor restriction of the kernel construction set and the bagging of kernels. The performance as a statistical estimator is examined through real and synthetic datasets. We provide an "optimization-free" parameter selection rule for our method, a theory of how our method works and a computational cost analysis. To demonstrate the usefulness as a population synthesizer, our method is applied to a household synthesis task for an urban micro-simulator.Comment: 10 pages, 4 figures, IEEE International Conference on Data Mining (ICDM) 201

The role of the colors of interior accessories in forming an impression of a room

When purchasing home interior furniture and accessories, several factors determine the color of the items that are chosen. People imagine how the item will look when actually placed in the room, anticipating whether it will fit in with the room or create the image they want. In order to help people make such decisions during shopping in stores and online, we analyzed the relationship between room color and item color. We prepared a photo of a home interior, processed the color of one item in the photo, and asked subjects about their impression of it. The color of the item was chosen from the colors used in the picture so that it could work in harmony with the room. Through the experiment, we found that even the color of a small item can affect the impression of an entire room

What kind of information attracts consumers' attention? Studying the differences in the amount of information on the landing page of a product

This study aimed to determine the amount of information that is interesting to users on a landing page for the purpose of purchasing a product. In this study, we clarified the amount and kind of information that attracts the interest of users. Specifically, we created three types of landing pages with different amounts of information for three products, and conducted an impression evaluation questionnaire. The three types of information on the landing pages were: (1) product image, product description, and catch copy, (2) one with additional campaign information, and (3) one with additional product sales results and satisfaction levels. Using the impression evaluation data obtained after the questionnaire survey, we conducted a factor analysis for each product and analyzed the factors that influenced the evaluation. Additionally, we found out the website design that made a difference in the willingness to purchase the products and the evaluation items that caused the difference using a t-test. It was found that users' willingness to purchase the product increased when data such as campaign information and actual results were included, and that reliability affected the willingness to purchase, especially for products that are used for a long time

Bezier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-objective Optimization

Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimization problem often forms an (M-1)-dimensional topological simplex (a curved line for M=2, a curved triangle for M=3, a curved tetrahedron for M=4, etc.). Since the dimensionality of the solution set increases as the number of objectives grows, an exponentially large sample size is needed to cover the solution set. To reduce the required sample size, this paper proposes a Bezier simplex model and its fitting algorithm. These techniques can exploit the simplex structure of the solution set and decompose a high-dimensional surface fitting task into a sequence of low-dimensional ones. An approximation theorem of Bezier simplices is proven. Numerical experiments with synthetic and real-world optimization problems demonstrate that the proposed method achieves an accurate approximation of high-dimensional solution sets with small samples. In practice, such an approximation will be conducted in the post-optimization process and enable a better trade-off analysis.Comment: To appear in AAAI 201