Kenta Mikawa

A proposal of extended cosine measure for distance metric learning in text classification

This paper discusses a new similarity measure between documents on a vector space model from the view point of distance metric learning. The documents are represented by points in the vector space by using the information of frequencies of words appearing in each document. The similarity measure between two different documents is useful to recognize the relationship and can be applied to classification or clustering of the data. Usually, the cosine similarity and the Euclid distance have been used in order to measure the similarity between points in the Euclidean space. However, these measures do not take the correlation among words which appear in documents into consideration on an application of the vector space model to document analysis. Generally speaking, many words which appear in documents have correlation to one another depending on the sentence structures, topics and subjects. Therefore, it is effective to build a suitable metric measure taking the correlation of words into consideration on the vector space in order to improve the performance of document classification and clustering. This paper presents a new effective method to acquire a distance measure on the document vector space based on an extended cosine measure. In addition, the way of distance metric learning is proposed to acquire the proper metric from the view point of supervised learning. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of the customer review which is posted on the web site and the newspaper article.

Collaborative filtering analysis of consumption behavior based on the latent class model

In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior (or evaluation) of customers (or users) and services (or items) for marketing. Assuming that each customer and service have the invisible attribute, which is called latent class, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer and service. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation and analyzing actual data.

Collaborative Filtering Based on the Latent Class Model for Attributes

In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation.

Distance metric learning using each category centroid with nuclear norm regularization

The development in information technology has resulted in more diverse data characteristics and a larger data scale. Therefore, pattern recognition techniques have received significant interest in various fields. In this study, we focus on a pattern recognition technique based on distance metric learning, which is known as the learning method in metric matrix under an arbitrary constraint from the training data. This method can acquire the distance structure, which takes account of the statistical characteristics of the training data. Most distance metric learning methods estimate the metric matrix from pairs of training data. One of the problem of the distance metric learning is that the computational complexity for prediction (i. e. distance calculation) is relatively high especially when the dimension of input data becomes large. To calculate the distance effectively, we propose the way to derive low rank metric matrix with nuclear norm regularization. When solving the optimization problem, we use the alternating direction method of multiplier and proximal gradient. To verify the effectiveness of our proposed method from the viewpoint of classification accuracy and rank reduction, simulation experiments using benchmark data sets are conducted.

Data pair selection for accurate classification based on information-theoretic metric learning

Data classification is one of the main technique in data analysis which has become more and more important in various fields of business. Automatic classification is the problem that classification category label is learned from training data. One of the effective approaches for automatic classification is the k-nearest neighbour (kNN) method based on distances between data pairs, combining with the well-known distance metric learning. In this study, we focus on information-theoretic metric learning (ITML) method. In ITML, the optimisation problem is formulated as learning metric matrix so that the distance between each pair of data belonging to the same class becomes smaller than a constant, while the distance between each pair of data belonging to different classes becomes larger than the other constant. In this study, we propose an improved procedure by choosing the data-pairs which affect clarifying the boundaries effectively. We verify the effectiveness of our proposed method by conducting the simulation experiment with benchmark dataset.

A Study of Distance Metric Learning by Considering the Distances between Category Centroids

In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

A modified aspect model for simulation analysis

This paper proposes a new latent class model to represent user segments in a marketing model of electric commerce sites. The aspect model proposed by T. Hofmann is well known and is also called the probabilistic latent semantic indexing (PLSI) model. Although the aspect model is one of effective models for information retrieval, it is difficult to interpret the meaning of the probability of latent class in terms of marketing models. It is desirable that the probability of latent class means the size of customer segment for the purpose of marketing research. Through this formulation, the simulation analysis to dissect the several situations become possible by using the estimated model. The impact of the strategy that we contact to the specific customer segment and make effort to increase the number of customers belonging to this segment can be predicted by using the model demonstrating the size of customer segment. This paper proposes a new model whose probability parameter of latent variable means the rate of users with the same preference in market. By applying the proposed model to the data of an internet portal site for job hunting, the effectiveness of our proposal is verified.

A proposal of l 1 regularized distance metric learning for high dimensional sparse vector space

In this paper, we focus on pattern recognition based on the vector space model with the high dimensional and sparse data. One of the pattern recognition methods is metric learning which learns a metric matrix by using the iterative optimization procedure. However most of the metric learning methods tend to cause overfitting and increasing computational time for high dimensional and sparse settings. To avoid these problems, we propose the method of l1 regularized metric learning by using the algorithm of alternating direction method of multiplier (ADMM) in the supervised setting. The effectiveness of our proposed method is clarified by classification experiments by using the Japanese newspaper article and UCI machine learning repository. And we show proposed method is the special case of the statistical sparse covariance selection.