Factorial Hidden Markov Models

Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

Understanding and modeling disturbances play a critical part in designing effective advanced model-based control solutions. Existing linear, stationary disturbance models are oftentimes limiting in the face of time-varying characteristics typically witnessed in process industries. These include intermittent drifts, abrupt changes, temporary oscillations, outliers and the likes. This work proposes a Hidden Markov Model-based framework to deal with such situations that exhibit discrete, modal behavior. The usefulness of the proposed disturbance framework – from modeling to ensuring the integral action under a wide variety of scenarios – is demonstrated through several examples.     

Tree-Based Covariance Modeling of Hidden Markov Models

In this paper, we present a tree-based, full covariance hidden Markov modeling technique for automatic speech recognition applications. A multilayered tree is built first to organize all covariance matrices into a hierarchical structure. Kullback–Leibler divergence is used in the tree-building to measure inter-Gaussian distortion and successive splitting is used to construct the multilayer covariance tree. To cope with the data sparseness problem in estimating a full covariance matrix, we interpolate the diagonal covariance matrix of a leaf-node at the bottom of the tree with the full covariance of its parent and ancestors along the path up to the root node. The interpolation coefficients are estimated in the maximum likelihood sense via the EM algorithm. The interpolation is performed in three different parametric forms: 1) inverse covariance matrix, 2) covariance matrix, and 3) off-diagonal terms of the full covariance matrix. The proposed algorithm is tested in three different databases: 1) the DARPA Resource Management (RM), 2) the Switchboard, and 3) a Chinese dictation. In all three databases, we show that the proposed tree-based full covariance modeling consistently performs better than the baseline diagonal covariance modeling. The algorithm outperforms other covariance modeling techniques, including: 1) the semi-tied covariance modeling (STC), 2) heteroscedastic linear discriminant analysis (HLDA), 3) mixtures of inverse covariance (MIC), and 4) direct full covariance modeling.     

基于隐马尔可夫模型的指纹分类研究

指纹分类是针对大型指纹库的一个重要的索引方式，可以有效地提高指纹匹配的效率．指纹类型的不同表现为指纹纹理结构的差异，而指纹的方向场则可以有效地描述纹理结构的差异．同一类型指纹不同区域上方向角结构的差异以及相邻区域间方向角结构的联系可以视作一个马尔可夫随机场．本文利用嵌入式隐马尔可夫模型对指纹方向场进行建模分析，通过合理地抽取指纹的类型特征，构造观察向量、进行建模训练，然后利用训练好的马尔可夫模型进行匹配，最终提出并实现了一种新的鲁棒性强且精度较高的指纹分类方法．     

Additive and Nonadditive Fuzzy Hidden Markov Models

We present a novel approach for the development of fuzzy hidden Markov models (FHMMs) by exploiting both additive and nonadditive properties of input fuzzy sets in the fuzzy rules of generalized fuzzy model (GFM). This development utilizes 1) Gaussian mixture model (GMM) to manipulate the mixture parameters for the input fuzzy sets and 2) GFM rules for the inclusion of states in the consequent part to be able to use HMM. Taking the components of Gaussian mixture density conditioned on the past system states and making use of equivalence of GMM with GFM, parameters of the additive and nonadditive FHMMs are estimated using the forward–backward procedure of the Baum–Welch algorithm. The additive and nonadditive FHMMs are validated on three benchmark applications involving time-series prediction, and the results are compared and found to be better than or equal to those of the existing recent fuzzy models.      

Hidden Markov Models for Software Piracy Detection

Abstract

In this paper, we analyze a method for detecting software piracy. A metamorphic generator is used to create morphed copies of a base piece of software. A hidden Markov model is trained on the opcode sequences extracted from these morphed copies and the resulting trained model is used to score suspect software to determine its similarity to the base software. A high score indicates that the suspect software may be a modified version of the base software, suggesting that further investigation is warranted. In contrast, a low score indicates that the suspect software differs significantly from the base software. We show that our approach is robust, in the sense that the base software must be extensively modified before it is not detected.     

基于隐马尔可夫模型的音乐分类

音乐类型（Genre）是应用最普遍的管理数字音乐数据库的方式,提出一种基于隐马尔可夫模型（Hidden Markov Models,HMMs）的音乐自动分类方案。在考虑传统的音色特征（Timbre）的同时,将另一重要特征节奏（Tempo）也加以考虑,并通过bagging训练两组HMM进行分类,达到了良好的效果。从结构、状态数和混合高斯模型数三个方面进行了参数优化,找到了最佳的HMM参数。在音乐数据集GTZAN上对传统模型和新模型分类效果进行了测试,结果表明考虑了节奏特征的HMM分类效果更佳。     

隐马尔可夫树模型在纹理分析中的应用

该文在研究小波域隐马尔可夫模型(HMM)的基础上,提出了一种全新隐马尔可夫树(HMT)模型.在以往的研究中,HMT通常将2维DWT的三个子带HL、LH、HH视作相互独立,形成三棵独立的子树分别建模.为了更好地描述三个子带间小波系数的相关性,该文将这三个子带中相应节点进行捆绑,作为一棵树进行建模.另外,对于每个尺度中的小波系数分布,HMT常用高斯混合分布来拟合.该文研究了基于泊松分布的统计建模方法(PHMT).纹理图像经Haar小波变换和乘数分解后,再采用PHMT建模.经过实验验证,基于泊松分布的统计建模方法是有效的.     

The Realization Problem for Hidden Markov Models

If {X _t} is a finite-state Markov process, and {Y _t} is a finite-valued output process with Y _t+1 depending (possibly probabilistically) on X _t, then the process pair is said to constitute a hidden Markov model. This paper considers the realization question: given the probabilities of all finite-length output strings, under what circumstances and how can one construct a finite-state Markov process and a state-to-output mapping which generates an output process whose finite-length strings have the given probabilities? After reviewing known results dealing with this problem involving Hankel matrices and polyhedral cones, we develop new theory on the existence and construction of the cones in question, which effectively provides a solution to the realization problem. This theory is an extension of recent theoretical developments on the positive realization problem of linear system theory. Date received: December 13, 1996. Date revised: October 9, 1998.     

Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models

This article presents a new algorithm to recognize natural distinctive places such as corridors, halls, narrowings, corridors with doors opening on the left side, etc., from indoor environments using Hidden Markov Models (HMM). HMM give a stochastic solution which can be used to make decisions on localization, navigation and path-planning. The environment is modeled as a topo-geometric map which combines topological and geometric information. This map is obtained from a Voronoi diagram using measurements of a laser telemeter. The characteristics of topo-geometric map (nodes, number of edges adjacent to nodes, slope of edges, etc.) are used to learn and to recognize the different places typical of indoor environments. This map can be used in order to resolve several problems in robotics such as localization, navigation and path-planning. Our method of place recognition is a fast and effective way for a robot to recognize typical places of indoor environments from a topo-geometric map.     

基于E-HMM的指纹匹配研究

利用嵌入式隐马尔可夫模型（Embedded Hidden Markov Models，E—HMM）对指纹方向场进行建模分析，通过合理地抽取指纹的类型特征，构造观察向量、进行建模训练，然后利用训练好的马尔可夫模型进行匹配，提出并实现了一种新的鲁棒性强且精度较高的指纹匹配方法。     

A spectral algorithm for learning Hidden Markov Models

Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computationally hard (under cryptographic assumptions), and practitioners typically resort to search heuristics which suffer from the usual local optima issues. We prove that under a natural separation condition (bounds on the smallest singular value of the HMM parameters), there is an efficient and provably correct algorithm for learning HMMs. The sample complexity of the algorithm does not explicitly depend on the number of distinct (discrete) observations—it implicitly depends on this quantity through spectral properties of the underlying HMM. This makes the algorithm particularly applicable to settings with a large number of observations, such as those in natural language processing where the space of observation is sometimes the words in a language. The algorithm is also simple, employing only a singular value decomposition and matrix multiplications.     

Using Hidden Markov Models for paper currency recognition

Accurate characterization is an important issue in paper currency recognition system. This paper proposes a robust paper currency recognition method based on Hidden Markov Model (HMM). By employing HMM, the texture characteristics of paper currencies are modeled as a random process. The proposed algorithm can be used for distinguishing paper currency from different countries. A similarity measure has been used for the classification in the proposed algorithm. To evaluate the performance of the proposed algorithm, experiments have been conducted on more than 100 denominations from different countries. The results indicate 98% accuracy for recognition of paper currency.     

隐Markov模型的多算法交叉耦合自适应算法

以离子通道信号重构为例,扩展HMM为矢量隐Markov模型,利用随机逼近原理对期望最大算法进行自适应改造,估计离子通道的动力学特征参数;递归辅助变量算法估计背景噪声的统计特征;卡尔曼滤波预测背景噪声;三种算法交叉耦合重构离子通道信号。该算法能够克服滤波器和背景噪声的影响,在低信噪比情况下得到了较高精度的估计参数和重构信号,具有鲁棒性和一致收敛特性。     

对隐马尔科夫模型中动态编程的探讨

主要对隐马尔科夫模型中的三个问题（估计问题、译码问题、学习问题）中的译码问题作了研究．并对其应用进行了探讨。     

基于拉普拉斯脸和隐马尔可夫的视频人脸识别

提出了一种基于拉普拉斯脸和隐马尔可夫模型的视频人脸识别方法。在训练过程中，采用拉普拉斯脸方法将每一视频序列中的人脸图像映射到拉普拉斯空间，将降维后的特征作为观测值，通过隐马尔可夫模型得到每一训练视频的统计特性和时间动态特性。在识别过程中，用每一个训练视频的隐马尔可夫模型来分析测试视频的时间动态特性，计算出每一训练模型产生该序列的概率，概率最大值所对应的模型就是待识别序列所属的类别。实验结果表明，该方法能够很好地进行视频人脸识别。     

IP包间关联异常检测的免疫隐马尔柯夫模型

数据包是网络通信的基本单位,网络上的入侵行为都应该在数据包中以不同的形式存在、表达着,而且网络中流通的数据包之间必然存在一定的关联性。人工免疫以往的研究多集中在对单个数据包的分析,很难察觉到隐蔽的、缓慢的入侵行为（这些行为的数据包序列间大多有一定的关联性）。因此,该文对于相互联系的多个数据包,挖掘出它们之间的关联特征,并针对这些特征采用了隐马尔柯夫模型的自动机识别器来检测入侵。