A bilevel framework for joint optimization of session compensation and classification for speaker identification

The i-vector framework based system is one of the most popular systems in speaker identification (SID). In this system, session compensation is usually employed first and then the classifier. For any session-compensated representation of i-vector, there is a corresponding identification result, so that both the stages are related. However, in current SID systems, session compensation and classifier are usually optimized independently. An incomplete knowledge about the session compensation to the identification task may lead to involving uncertainties. In this paper, we propose a bilevel framework to jointly optimize session compensation and classifier to enhance the relationship between the two stages. In this framework, we use the sparse coding (SC) to obtain the session-compensated feature by learning an overcomplete dictionary, and employ the softmax classifier and support vector machine (SVM) in classifying respectively. Moreover, we present a joint optimization of the dictionary and classifier parameters under a discriminative criterion for classifier with conditions for SC. In addition, the proposed methods are evaluated on the King-ASR-010, VoxCeleb and RSR2015 databases. Compared with typical session compensation techniques, such as linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA), our methods can be more robust to complex session variability. Moreover, compared with the typical classifiers in i-vector framework, i.e. the cosine distance scoring (CDS) and probabilistic linear discriminant analysis (PLDA), our methods can be more suitable for SID (multiclass task).     

A unified framework for consistency check

Constraint Satisfaction Problem (CSP) involves finding values for variables to satisfy a set of constraints. Consistency check is the key technique in solving this class of problems. Past research has developed many algorithms for such a purpose, e.g., node consistency, are consistency, generalized node and arc consistency, specific methods for checking specific constraints, etc. In this article, an attempt is made to unify these algorithms into a common framework. This framework consists of two parts. the first part is a generic consistency check algorithm, which allows and encourages each individual constraint to be checked by its specific consistency methods. Such an approach provides a direct way of practical implementation of the CSP model for real problem-solving. the second part is a general schema for describing the handling of each type of constraint. the schema characterizes various issues of constraint handling in constraint satisfaction, and provides a common language for expressing, discussing, and exchanging constraint handling techniques. © 1995 John Wiley & Sons, Inc.     

A unified framework for heterogeneous patterns

Knowledge patterns, such as association rules, clusters or decision trees, can be defined as concise and relevant information that can be extracted, stored, analyzed, and manipulated by knowledge workers in order to drive and specialize business decision processes. In this paper we deal with data mining patterns. The ability to manipulate different types of patterns under a unified environment is becoming a fundamental issue for any ‘intelligent’ and data-intensive application. However, approaches proposed so far for pattern management usually deal with specific and predefined types of patterns and mainly concern pattern extraction and exchange issues. Issues concerning the integrated, advanced management of heterogeneous patterns are in general not (or marginally) taken into account.     

A unified framework for multimodal retrieval

In this paper, a unified framework for multimodal content retrieval is presented. The proposed framework supports retrieval of rich media objects as unified sets of different modalities (image, audio, 3D, video and text) by efficiently combining all monomodal heterogeneous similarities to a global one according to an automatic weighting scheme. Then, a multimodal space is constructed to capture the semantic correlations among multiple modalities. In contrast to existing techniques, the proposed method is also able to handle external multimodal queries, by embedding them to the already constructed multimodal space, following a space mapping procedure of a submanifold analysis. In our experiments with five real multimodal datasets, we show the superiority of the proposed approach against competitive methods.     

A unified framework for structure identification

We propose a general framework for structure identification, as defined by Dechter and Pearl. It is based on the notion of prime implicate, and handles Horn, bijunctive and affine, as well as Horn-renamable formulas, for which, to our knowledge, no polynomial algorithm has been proposed before. This framework, although quite general, gives good complexity results, and in particular we get for Horn formulas the same running time and better output size than the algorithms previously known.     

一种新的联合块对角化卷积盲分离时域算法

提出一种基于高阶累积量联合块对角化的时域算法求解卷积混合盲信号分离问题。引入白化处理,将混叠矩阵转变成酉矩阵,混合信号转变为互不相关的,进而计算出其对应的一系列高阶累积量矩阵,通过最小化代价函数来实现高阶累积量矩阵联合块对角化的目的,在时域中解决超定卷积盲分离问题。实验表明,相比于经典的自然梯度算法,所提方法的分离精度更高,且运算速度也更快。     

A unified framework for adaptive filter algorithms with variable step-size

Employing a recently introduced framework within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, we extend this framework to cover block normalized LMS (BNLMS) and normalized data reusing LMS (NDRLMS) adaptive filter algorithms. Accordingly, we develop a generic variable step-size adaptive filter. Variable step-size normalized LMS (VSSNLMS) and VSS affine projection algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic variable step-size adaptive filter. In this paper we introduce two new VSS adaptive filter algorithms named the variable step-size BNLMS (VSSBNLMS) and the variable step-size NDRLMS (VSSNDRLMS) based on the generic VSS adaptive filter. The proposed algorithms show the higher convergence rate and lower steady-state mean square error compared to the ordinary BNLMS and NDRLMS algorithms.     

A unified SVM framework for signal estimation

This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has gained popularity in recent years due to its advantages over other methods: SVMs are flexible non-linear methods that are intrinsically regularized and work well in low-sample-sized and high-dimensional problems. SVMs can be designed to take into account different noise sources in the formulation and to fuse heterogeneous information sources. Nevertheless, the use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of several properties of Mercerʼs kernels and functional analysis to develop a family of SVM methods for estimation in DSP. Three types of signal model equations are analyzed. First, when a specific time-signal structure is assumed to model the underlying system that generated the data, the linear signal model (so-called Primal Signal Model formulation) is first stated and analyzed. Then, non-linear versions of the signal structure can be readily developed by following two different approaches. On the one hand, the signal model equation is written in Reproducing Kernel Hilbert Spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercerʼs kernels are readily used in SVM non-linear algorithms. On the other hand, in the alternative and not so common Dual Signal Model formulation, a signal expansion is made by using an auxiliary signal model equation given by a non-linear regression of each time instant in the observed time series. These building blocks can be used to generate different novel SVM-based methods for problems of signal estimation, and we deal with several of the most important ones in DSP. We illustrate the usefulness of this methodology by defining SVM algorithms for linear and non-linear system identification, spectral analysis, non-uniform interpolation, sparse deconvolution, and array processing. The performance of the developed SVM methods is compared to standard approaches in all these settings. The experimental results illustrate the generality, simplicity, and capabilities of the proposed SVM framework for DSP.     

A unified framework for managing Web-based services

The emergence of Web technologies enables a variety of Web-based service applications, which can be examined from business process integration, supply chain management, and knowledge management perspectives. To categorize existing Web-based services while foreseeing potential new types, a unified view is needed to represent the structures and processes of Web-based services. This paper proposes a general framework to identify essential structures and operations of Web-based services, and then models these components. We articulate the framework with Web technologies, such as Web service and semantic Web, multi-agent and peer-to-peer, and Web information retrieval and mining. Two comprehensive examples in insurance and knowledge services are used to elaborate the use of Web-based service framework in fulfilling business processes. This study synthesizes essential structures and processes of Web-based services to build a framework for researchers and practitioners to develop Web-based services and techniques.     

A unified framework for semi-supervised dimensionality reduction

In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under the framework, several classical methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), maximum margin criterion (MMC), locality preserving projections (LPP) and their corresponding kernel versions can be seen as special cases. For high-dimensional data, we can give a low-dimensional embedding result for both discriminating multi-class sub-manifolds and preserving local manifold structure. Experiments show that our algorithms can significantly improve the accuracy rates of the corresponding supervised and unsupervised approaches.     

A unified tensor framework for face recognition

In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion.     

A unified framework for semi-supervised PU learning

Traditional supervised classifiers use only labeled data (features/label pairs) as the training set, while the unlabeled data is used as the testing set. In practice, it is often the case that the labeled data is hard to obtain and the unlabeled data contains the instances that belong to the predefined class but not the labeled data categories. This problem has been widely studied in recent years and the semi-supervised PU learning is an efficient solution to learn from positive and unlabeled examples. Among all the semi-supervised PU learning methods, it is hard to choose just one approach to fit all unlabeled data distribution. In this paper, a new framework is designed to integrate different semi-supervised PU learning algorithms in order to take advantage of existing methods. In essence, we propose an automatic KL-divergence learning method by utilizing the knowledge of unlabeled data distribution. Meanwhile, the experimental results show that (1) data distribution information is very helpful for the semi-supervised PU learning method; (2) the proposed framework can achieve higher precision when compared with the state-of-the-art method.     

A unified framework for subspace face recognition

PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.     

A unified framework for market segmentation and its applications

Market segmentation is a core marketing concept that is conceptually simple to define and understand, but inherently a multi-criteria problem that is hard to measure and computationally difficult in many aspects. This paper reviews the development of market segmentation techniques and identifies the computational issues of the applications of market segmentation. A multidimensional unified framework for market segmentation is proposed based on the relationship among segmentation variables, data measures, and the multi-objective optimization techniques implemented. We conduct an empirical comparison of two prominent methods: a concomitant finite mixture model and a multi-objective evolutionary algorithm. The result shows that the proposed framework helps to understand different segmentation models and solutions and to guide the development of new market segmentation solution techniques.     

A Bayesian framework for sensory adaptation

Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.     

A framework for an integrated unified modeling language

The unified modeling language (UML) is one of the most commonly used modeling languages in the software industry. It simplifies the complex process of design by providing a set of graphical notations, which helps express the objectoriented analysis and design of software projects. Although UML is applicable to different types of systems, domains, methods, and processes, it cannot express certain problem domain needs. Therefore, many extensions to UML have been proposed. In this paper, we propose a framework for integrating the UML extensions and then use the framework to propose an integrated unified modeling language-graphical (iUML-g) form. iUML-g integrates the existing UML extensions into one integrated form. This includes an integrated diagram for UML class, sequence, and use case diagrams. The proposed approach is evaluated using a case study. The proposed iUML-g is capable of modeling systems that use different domains.