Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper first reviews the root methods and theories on multi-view representation learning, especially on canonical correlation analysis (CCA) and its several extensions. And then we investigate the advancement of multi-view representation learning that ranges from sh...

This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking...

In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is ...

For continuous multi-objective optimization problems there exists an infinite number of solutions on the Paretooptimal front. A multi-objective evolutionary algorithm attempts to find a representative set of the Pareto-optimal solutions. In the case of multi-objective multi-modal problems, there exist multiple decision vectors which map to identical objective vectors on Pareto front. Many multi...

Multi-modal test execution allows execution of the same test against various layers of a software system, e.g. the GUI layer, the web service layer and the business logic layer. Multi-modal test execution helps with identifying the location of software bugs during debugging and maintenance as well as in tracking the progress of the development effort. This paper presents a method that effective...

Complex objects are usually with multiple modal features. In multi-modal learning, modalities closely related to the target tasks are known as strong modalities. While collecting strong modalities of all instances is often expensive, and current multi-modal learning techniques hardly take the strong modal feature extraction expenses into consideration. On the other hand, active learning is prop...

In environments with information symmetry, uncertain or ambiguous data associations can lead to a multi-modal hypothesis on the robot’s state. Thus, a planner cannot simply base actions on the most-likely state. We propose an algorithm that uses a Receding Horizon Planning approach to plan actions that sequentially disambiguate a multi-modal belief to a uni-modal Gaussian and achieve tight loca...

Despite half a century of fuzzy sets and fuzzy logic progress, as fuzzy sets address complex and uncertain information through the lens of human knowledge and subjectivity, more progress is needed in the semantics of fuzzy sets and in exploring the multi-modal aspect of fuzzy logic due to the different cognitive, emotional and behavioral angles of assessing truth. We lay here the foundations of...

This paper presents an overview of the spoken and multimodal dialog Portuguese corpora collected in the context of the FASiL (Flexible and Adaptive Spoken Language and Multi-Modal Interfaces) project. The project developed a Virtual Personal Assistant application in the Personal Information Management domain, exploiting the state-of-theart of speech and multi-modal technology. The FASiL corpora...

Multi-modal virtual environments succeed better, than single-channel technologies, in creating the 'sense of presence'. We hypothesize that the underlying cognitive mechanism is related to a faster mental processing of multimodal events. Comparing reaction time in uni-modal, bimodal and tri-modal events, we show that the processing speed is: uni-modal > bi-modal > tri-modal. Given this advantag...