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The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

We propose a probabilistic model for matching clusters in different domains without correspondence information.The proposed method can handle data with more than two domains, and the number of objects in each domain can be different.We extend the proposed method for a semi-supervised setting.We demonstrate that the proposed method achieve better matching performance than existing methods using synthetic and real-world data sets. Object matching is an important task for finding the correspondence between objects in different domains, such as documents in different languages and users in different databases. In this paper, we propose probabilistic latent variable models that offer many-to-many matching without correspondence information or similarity measures between different domains. The proposed model assumes that there is an infinite number of latent vectors that are shared by all domains, and that each object is generated from one of the latent vectors and a domain-specific projection. By inferring the latent vector used for generating each object, objects in different domains are clustered according to the vectors that they share. Thus, we can realize matching between groups of objects in different domains in an unsupervised manner. We give learning procedures of the proposed model based on a stochastic EM algorithm. We also derive learning procedures in a semi-supervised setting, where correspondence information for some objects are given. The effectiveness of the proposed models is demonstrated by experiments on synthetic and real data sets.

Brain-related researches encompass many fields of studies and usually involve worldwide collaborations. Recognizing the value of these international collaborations for efficient use of resources and improving the quality of brain research, the International Neuroinformatics Coordinating Facility (INCF) started to coordinate the effort of establishing neuroinformatics (NI) centers and portal sites among the different participating countries. These NI centers and portal sites will serve as the conduit for the interchange of information and brain-related resources among different countries. In Japan, several NI platforms under the support of NIJC (NI Japan Center) are being developed with one platform called, Visiome, already operating and publicly accessible at “ http://www.platform.visiome.org ”. Each of these platforms requires their own set of keywords that represent important terms covering their respective fields of study. One important function of this predefined keyword list is to help contributors classify the contents of their contributions and group related resources. It is vital, therefore, that this predefined list should be properly chosen to cover the necessary areas. Currently, the process of identifying these appropriate keywords relies on the availability of human experts which does not scale well considering that different areas are rapidly evolving. This problem prompted us to develop a tool to automatically filter the most likely terms preferred by human experts. We tested the effectiveness of the proposed approach using the abstracts of the Vision Research Journal (VR) and Investigative Ophthalmology and Visual Science Journal (IOVS) as source files.

This paper presents a classifier for text data samples consisting of main text and additional components, such as Web pages and technical papers. We focus on multiclass and single-labeled text classification problems and design the classifier based on a hybrid composed of probabilistic generative and discriminative approaches. Our formulation considers individual component generative models and constructs the classifier by combining these trained models based on the maximum entropy principle. We use naive Bayes models as the component generative models for the main text and additional components such as titles, links, and authors, so that we can apply our formulation to document and Web page classification problems. Our experimental results for four test collections confirmed that our hybrid approach effectively combined main text and additional components and thus improved classification performance.

We propose a new method for analyzing multivariate time-series data governed by competitive dynamics such as fluctuations in the number of visitors to Web sites that form a market. To achieve this aim, we construct a probabilistic dynamical model using a replicator equation and derive its learning algorithm. This method is implemented for both categorizing the sites into groups of competitors and predicting the future shares of the sites based on the observed time-series data. We confirmed experimentally, using synthetic data, that the method successfully identifies the true model structure, and exhibits better prediction performance than conventional methods that leave competitive dynamics out of consideration. We also experimentally demonstrated, using real data of visitors to 20 Web sites offering streaming video contents, that the method suggested a reasonable competition structure that conventional methods failed to find and that it outperformed them in terms of predictive performance.

In this paper, we propose a new network growth model and its learning algorithm to more precisely model such a real-world growing network as the Web. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We show that the proposed model exhibits a degree distribution with a power-law tail, which is an important characteristic of many large-scale real-world networks including the Web. Using real Web data, we experimentally show that predictive ability can be improved by incorporating directional attachment and community structure. Also, using synthetic data, we experimentally show that predictive ability can definitely be improved by incorporating community structure.

This paper presents a deterministic annealing EM (DAEM) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional EM algorithm. In our approach, a new posterior parameterized by `temperature' is derived by using the principle of maximum entropy and is used for controlling the annealing process. In the DAEM algorithm, the EM process is reformulated as the problem of minimizing the thermodynamic free energy by using a statistical mechanics analogy. Since this minimization is deterministically performed at each temperature, the total search is executed far more efficiently than in the simulated annealing. Moreover, the derived DAEM algorithm, unlike the conventional EM algorithm, can obtain better estimates free of the initial parameter values. We also apply the DAEM algorithm to the training of probabilistic neural networks using mixture models to estimate the probability density and demonstrate the performance of the DAEM algorithm.

A new competitive learning approach is presented for optimal vector quantizer design. First, it is shown that the original CL algorithm is equivalent to the traditional nonconnectionist VQ design algorithm called the LBG algorithm. Then, it is shown that the conventional conscience principle or equiprobable principle is not optimal from the standpoint of the minimization of the expected distortion. Next, a basic principle called the equidistortion principle for the design of optimal vector quantizers is theoretically derived by using Gersho's asymptotic theory. This paper proposes a new competitive learning algorithm with a selection mechanism, called the CSL (competitive and selective learning) algorithm, which is based on the equidistortion principle. Because the selection mechanism enables the system to escape from local minima, the proposed algorithm can obtain better performance without a particular initialization procedure even when the input data cluster in a number of regions in the input vector space. Simulation results comparing the performance of the CSL algorithm with other conventional algorithms for synthetic and real-world data show that the CSL algorithm, in spite of its simplicity, always produces the best quantizers with the least distortion, regardless of the initial codes. The optimality of the proposed CSL algorithm is also verified through a synthetic one-dimensional quantizer problem.