Independent component analysis (ICA) deals with the problem of estimating unknown latent variables (independent components) from observed data. One of the previous studies of ICA assumes a Laplace distribution on independent components. However, this assumption makes it difficult to calculate the posterior distribution of independent components. On the other hand, in the problem of sparse linear regression, several studies have approximately calculated the posterior distribution of parameters by assuming a hierarchical model expressing a Laplace distribution. This paper considers ICA in which a hierarchical model expressing a Laplace distribution is assumed on independent components. For this hierarchical model, we propose a method of calculating the approximate posterior distribution of independent components by using a variational Bayes method. Through some experiments, we show the effectiveness of our proposed method.

In this study, we develop a new algorithm for decoding binary linear codes for symbol-pair read channels. The symbol-pair read channel was recently introduced by Cassuto and Blaum to model channels with higher write resolutions than read resolutions. The proposed decoding algorithm is based on linear programming (LP). For LDPC codes, the proposed algorithm runs in time polynomial in the codeword length. It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance d(fp) of the code, which is a lower bound on the minimum pair distance. It is proved that the proposed LP decoder corrects up to. inverted left perpendiculard(fp)/2inverted right perpendicular - 1 errors.

In this paper, we develop a new decoding algorithm of binary linear codes for symbol-pair read channel. The Symbol-pair read channel has recently been introduced by Cassuto and Blaum to model channel whose write resolution is higher than read resolution. The proposed decoding algorithm is based on the linear programming (LP). It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance d(fp) of the code which is a lower bound on the minimum pair distance. It is proved that the proposed LP decoder corrects up to [d(fp)/2] - 1 errors.

A new theory known as compressed sensing considers the problem to acquire and recover a sparse signal from its linear measurements. In this paper, we propose a new support recovery algorithm from noisy measurements based on the linear programming (LP). LP is widely used to estimate sparse signals, however, we focus on the problem to recover the support of sparse signals rather than the problem to estimate sparse signals themselves. First, we derive an integer linear programming (ILP) formulation for the support recovery problem. Then we obtain the LP based support recovery algorithm by relaxing the ILP. The proposed LP based recovery algorithm has an attracting property that the output of the algorithm is guaranteed to be the maximum a posteiori (MAP) estimate when it is integer valued. We compare the performance of the proposed algorithm to a state-of-the-art algorithm named sparse matching pursuit (SMP) via numerical simulations.

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products
their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

本稿ではプライバシー保護を目的とした回帰分析について扱う．これは複数のユーザがそれぞれ異なるデータを保持したもとで，それぞれの保持するデータを互いに公開，共有することなく，協力して全てのデータを用いた場合と同等の回帰分析を行うというものである．本稿ではまず，ユーザが2人の場合を想定し，2者が異なる属性に関するデータを保持しているもとで最小二乗推定量を分散計算する場合を考える．そのもとで，新たな分散計算プロトコルを提案し，プライバシーについてプロトコルの安全性を評価する．また，提案するプロトコルは繰り返し計算を行うため，数値実験によりプロトコルの繰り返し数についての評価を行う．さらに，収束回数を削減する修正プロトコルを提案し，多者間での分散計算への拡張も行う．

In this paper, we consider a completion problem of multiple related matrices. Matrix completion problem is the problem to estimate unobserved elements of the matrix from observed elements. It has many applications such as collaborative filtering, computer vision, biology, and so on. In cases where we can obtain some related matrices, we can expect that their simultaneous completion has better performance than completing each matrix independently. Collective matrix factorization is a powerful approach to jointly factorize multiple matrices. However, existing completion algorithms for the collective matrix factorization have some drawbacks. One is that most existing algorithms are based on non-convex formulations of the problem. Another is that only a few existing algorithms consider the strength of the relation among matrices and it results in worse performance when some matrices are actually not related. In this paper, we formulate the multiple matrix completion problem as the convex optimization problem. Moreover, it considers the strength of the relation among matrices. We also develop an optimization algorithm which solves the proposed problem efficiently based on the alternating direction method of multipliers (ADMM). We verify the effectiveness of our approach through numerical experiments on both synthetic data and real data set: MovieLens.

In this paper, we consider a completion problem of multiple related matrices. Matrix completion problem is the problem to estimate unobserved elements of the matrix from observed elements. It has many applications such as collaborative filtering, computer vision, biology, and so on. In cases where we can obtain some related matrices, we can expect that their simultaneous completion has better performance than completing each matrix independently. Collective matrix factorization is a powerful approach to jointly factorize multiple matrices. However, existing completion algorithms for the collective matrix factorization have some drawbacks. One is that most existing algorithms are based on non-convex formulations of the problem. Another is that only a few existing algorithms consider the strength of the relation among matrices and it results in worse performance when some matrices are actually not related. In this paper, we formulate the multiple matrix completion problem as the convex optimization problem. Moreover, it considers the strength of the relation among matrices. We also develop an optimization algorithm which solves the proposed problem efficiently based on the alternating direction method of multipliers (ADMM). We verify the effectiveness of our approach through numerical experiments on both synthetic data and real data set: MovieLens.

In this paper, we develop an iterative multiuser joint decoding of code-division multiple-access (CDMA) signals based on a distributed optimization algorithm. For the joint decoding problem, decoding algorithm based on the turbo principle is widely used. The algorithm consists of soft-input soft-output (SISO) channel decoder and SISO multiuser detector and it can be derived as an application of the sum-product algorithm. On the other hand, in the research area of error correcting codes, the decoding algorithm based on convex optimization has been attracting a great deal of attention. Decoding algorithm based on linear programming (LP) has decoding error rate which is comparable with sum-product algorithm with stronger theoretical guarantees. We formulate the joint decoding problem of CDMA signals as an convex optimization problem and we present a relax form of the problem. Moreover, we propose a distributed algorithm which efficiently solves the relaxed optimization problem. The proposed algorithm is based on alternating direction method of multipliers (ADMM). We also see the performance of the proposed decoder through numerical simulations. © 2013 IEEE.

In this paper, we develop an iterative multiuser joint decoding of code-division multiple-access (CDMA) signals based on a distributed optimization algorithm. For the joint decoding problem, decoding algorithm based on the turbo principle is widely used. The algorithm consists of soft-input soft-output (SISO) channel decoder and SISO multiuser detector and it can be derived as an application of the sum-product algorithm. On the other hand, in the research area of error correcting codes, the decoding algorithm based on convex optimization has been attracting a great deal of attention. Decoding algorithm based on linear programming (LP) has decoding error rate which is comparable with sum-product algorithm with stronger theoretical guarantees. We formulate the joint decoding problem of CDMA signals as an convex optimization problem and we present a relax form of the problem. Moreover, we propose a distributed algorithm which efficiently solves the relaxed optimization problem. The proposed algorithm is based on alternating direction method of multipliers (ADMM). We also see the performance of the proposed decoder through numerical simulations. © 2013 IEEE.

本稿ではプライバシー保護を目的とした回帰分析について扱う.まず,2者が異なる属性に関するデータを保持しているもとで,2者間でデータを渡すことなく回帰係数の最小二乗推定量を分散計算する新たな方式を提案し,プライバシーの安全性を評価する.また,提案するプロトコルは繰り返し計算を行うため,数値実験によりプロトコルの繰り返し数についての評価を行う.さらに,多者間での分散計算への拡張も行う.

2元線型符号を用いた多重アクセス通信路に対して,双対分解法に基づいた線形計画復号法を提案する.2元線型符号の最尤復号問題に対して,線形計画復号法はsum-product復号法に近い復号性能を持ち,更により理論的に強い保証が得られる復号法であり,干渉通信路や多重アクセス通信路など様々な通信路上での復号問題に応用されている.しかし,線形計画復号法は復号の際に線形計画問題を解く必要があり,一般的に線形計画法を解くのに必要な計算量は変数・制約の数に対して多項式オーダとなるため,Sum-Product復号法等の復号法と比べて復号に多くの時間を要するという問題がある.そこで,シングル-ユーザの無記憶通信路上での復号問題に対しては,様々な効率的な復号法に関する研究が数多くされている.特に,双対分解に基づいた復号法は符号長の長いLDPC符号に対して有効であることが先行研究により解明されている.本研究では,これらの研究を拡張し,多重アクセス通信路に対する復号問題に対する,双対分解に基づいた効率的な線形計画復号法を提案する.

F.P. Preparataらは,マルチコンピュータシステムの要素である各ノードを別のいくつかのノードが独立に検査を行い,それらの結果からシステム中の全ての故障ノードを発見する故障診断モデルを提案した.本論文では確率的に検査結果を誤る故障診断モデルを対象として,線形計画法を用いた故障診断法を示す.更に計算機シミュレーションによりその有効性を明らかにする.

F.P. Preparataらは,マルチコンピュータシステムの要素である各ノードを別のいくつかのノードが独立に検査を行い,それらの結果からシステム中の全ての故障ノードを発見する故障診断モデルを提案した.本論文では確率的に検査結果を誤る故障診断モデルを対象として,線形計画法を用いた故障診断法を示す.更に計算機シミュレーションによりその有効性を明らかにする.

We propose new algorithms for fault diagnosis problem based on the dual decomposition method and the augmented Lagrangian method. Our algorithms are convergent and those outputs are same as that of Linear Programming (LP) based fault diagnosis algorithm. The proposed algorithms have smaller computational complexity than ordinary LP solver. Experimental results show the practical potentials of the proposed algorithms.

A stream cipher is an important class of encryption algorithms. Its safety depends on the structure of the pseudo-random number generator used. There are various types of pseudo-random number generators in existence, and attack algorithms used on them have been studied individually. In this paper, we express the problem of attacks on a general stream cipher as a probabilistic inference problem, and formulate the optimal key estimation. We also propose a unified framework of attack algorithms that can be applied to a wide variety of stream ciphers. The optimal key estimation, however, has computational complexity. To reduce the complexity, an approximation algorithm based on a probabilistic inference is proposed. We also describe some attack algorithms used on practical pseudo-random number generators. Finally, the proposed algorithm is evaluated by through a computer simulation.

近年,デバイス内の不揮発性メモリに秘密情報を格納しておくことは,物理破壊攻撃やサイドチャネル攻撃等によって,秘密情報を漏洩してしまう危険性があると指摘されている.その解決法としてPhysical Unclonable Functions(PUF)が提案された.現在,PUFを利用した様々な暗号方式が提案されているが,その中でも代表的な暗号方式として認証が挙げられる.本研究では,PUFのチャレンジに対するレスポンスを確率分布として定義し,PUFを利用した認証を2値仮説検定問題として定式化を行い,認証者の認証誤り確率及び攻撃者のなりすまし攻撃成功確率を定義する.この時,PUFを利用した認証において認証誤り確率を0とした時のなりすまし攻撃成功確率の下界を導出する.また,半導体上に形成されるシリコンPUFの一つであるアービターPUFのチャレンジに対するレスポンスを具体的な確率分布で表現する.この時,認証誤り確率を0とした時のなりすまし攻撃成功確率の下界をシミュレーションにより導出し,アービターPUFの安全性について考察を行う.

共通鍵暗号の一種であるストリーム暗号は,鍵を擬似乱数生成器に入力することで得られる鍵系列と平文系列との排他的論理和をとることで暗号文系列を生成する暗号方式である.本研究では,ストリーム暗号への攻撃を統計的決定理論の枠組みから鍵推定問題として定式化し,べイズ基準に基づく最適な鍵推定方法について考える.また実際に用いられている擬似乱数生成器について確率モデルで表し,グラフィカルモデルで表現することで確率推論アルゴリズムに基づく鍵推定アルゴリズムを提案する.提案したアルゴリズムについてはシミュレーションによる評価を行い,ストリーム暗号の安全性について考察を行う.

In this paper, we develop linear-programming (LP) decoding for multiple-access channels with binary linear codes. For single-user channels, LP decoding has attracted much attention in recent years as a good approximation to maximum-likelihood (ML) decoding. We demonstrate how the ML decoding problem for multiple-access channels with binary linear codes can be formulated as an LP problem. This is not straightforward, because the objective function of the problem is generally a nonlinear function of the codeword symbols. We introduce auxiliary variables such that the objective function is a linear function of these variables. The ML decoding problem then reduces to the LP problem. As in the case for single-user channels, we formulate the relaxed LP problem to reduce the complexity for practical implementation, and as a result propose a decoder that has the desirable property known as the ML certificate property (i.e., if the decoder outputs an integer solution, the solution is guaranteed to be the ML codeword). Although the computational complexity of the proposed algorithm is exponential in the number of users, we can reduce this complexity for Gaussian multiple-access channels. Furthermore, we compare the performance of the proposed decoder with a decoder based on the sum-product algorithm. Copyright © 2011 The Institute of Electronics, Information and Communication Engineers.

In this paper, we develop linear-programming (LP) decoding for multiple-access channels with binary linear codes. For single-user channels, LP decoding has attracted much attention in recent years as a good approximation to maximum-likelihood (ML) decoding. We demonstrate how the ML decoding problem for multiple-access channels with binary linear codes can be formulated as an LP problem. This is not straightforward, because the objective function of the problem is generally a non-linear function of the codeword symbols. We introduce auxiliary variables such that the objective function is a linear function of these variables. The ML decoding problem then reduces to the LP problem. As in the case for single-user channels, we formulate the relaxed LP problem to reduce the complexity for practical implementation, and as a result propose a decoder that has the desirable property known as the ML certificate property (i.e., if the decoder outputs an integer solution, the solution is guaranteed to be the ML codeword). Although the computational complexity of the proposed algorithm is exponential in the number of users, we can reduce this complexity for Gaussian multiple-access channels. Furthermore, we compare the performance of the proposed decoder with a decoder based on the sum-product algorithm.

グラフィカルモデル上の確率推論の問題は,符号理論・画像処理・音声認識などの様々な工学上の問題に現れ重要である.近年,確率推論の応用の1つである誤り訂正符号の複号問題に対して,線形計画法に基づいた復号アルゴリズムに関する研究が盛んに行われている.誤り訂正符号の復号問題をファクターグラフにより表現すると,グラフ中に含まれる関数は,指示関数とそれ以外の関数(非指示関数)に分類される.特に指示関数は複数の変数ノードに接続し,非指示関数は単一の変数ノードのみに接続している.一般的な確率推論の問題をファクターグラフとして表現した場合,複数の変数ノードと接続する非指示関数がファクターグラフに含まれる場合がある.本研究では,このような問題に対して,線形計画法に基づいた推論アルゴリズムを構築することを目的とする.

Maximum likelihood (ML) decoding of linear block codes can be considered as an integer linear programming (ILP) Since it is an NP hard problem in general there are many researches about the algorithms to approximately solve the problem One of the most popular algorithms is linear programming (LP) decoding proposed by Feldman et al LP decoding is based on the LP relaxation which Is a method to approximately solve the ILP corresponding to the ML decoding problem Advanced algorithms for solving ILP (approximately or exactly) include cutting plane method and branch and bound method As applications of these methods adaptive LP decoding and branch and bound decoding have been proposed by Taghavi et al and Yang et al respectively Another method for solving ILP is the branch and cut method which is a hybrid of cutting plane and branch and bound methods The branch and cut method is widely used to solve ILP however it is unobvious that the method works well for the ML decoding problem In this paper we show that the branch and cut method is certainly effective for the ML decoding problem Furthermore the branch and cut method consists of some technical components and the performance of the algorithm depends on the selection of these components It is important to consider how to select the technical components in the branch and cut method We see the differences caused by the selection of those technical components and consider which scheme is most effective for the ML decoding problem through numerical simulations

2元線形符号を用いた多元接続通信路における線形計画法に基づいた復号法を提案する.近年,通信路符号化の最尤復号の問題に対して線形計画復号法が注目を浴びている.本研究ではまず,2元線形符号を用いた多元接続通信路における最尤復号の問題がどのようにして線形計画問題として定式化されるかを示す.これは,目的関数である対数尤度関数が,一般的には符号語シンボルに対する線形関数ではないため自明ではない.そこで,本研究では,目的関数がその変数の線形関数になるような補助変数を導入する.これにより,最尤復号の問題が線形計画問題として定式化されるが,この問題を解くには非常に多くの計算量を要し,現実的ではない.本研究では,1対1通信における線形計画復号法と同様に,この問題を緩和した線形計画問題を考える.提案された復号法は,1対1通信の場合と同様,復号結果が整数ならば最尤復号であることが保証されるという望ましい性質を持つ.提案された復号法は,通信路のユーザー数に対して指数オーダーの計算量が必要となるが,ガウス型多元接続通信路においてはその計算量を削減することが出来ることを示す.また,コンピュータシミュレーションにより,Sum-Productアルゴリズムに基づいた復号法との性能比較を行う.

Optimum detection for the multiuser code-division multiple-access channel is prohibitively complex. This paper considers new iterative multiuser detection algorithm based on the belief propagation algorithm. Previously, the idea to apply the belief propagation algorithm to multiuser detection problem was suggested , however, it was believed that to apply the belief propagation algorithm to the detection problem is impossible because it requires an exponentially large amount of computation. It was the only fact that the parallel interference canceller is derived as an approximation of the belief propagation. In this paper, we show that the belief propagation algorithm can be applied to the detection problem by converting the factor graph structure. Performance of the detector based on the belief propagation algorithm is better than that of the parallel interference canceller. © 2008 IEEE.