Finding correlations in multimodal data using decomposition approaches
نویسندگان
چکیده
In this paper, we propose the application of standard decomposition approaches to find local correlations in multimodal data. In a test scenario, we apply these methods to correlate the local shape of turbine blades with their associated aerodynamic flow fields. We compare several decomposition algorithms with regards to their efficiency at finding local correlations and their ability to predict one modality from another.
منابع مشابه
Decomposition of Multimodal Data for Affordance-based Identification of Potential Grasps
In this paper, we apply standard decomposition approaches to the problem of finding local correlations in multi-modal and high-dimensional grasping data, particularly to correlate the local shape of cup-like objects to their associated local grasp configurations. We compare the capability of several decomposition methods to establish these task-relevant, inter-modal correlations and indicate ho...
متن کاملCorrelating Shape and Functional Properties Using Decomposition Approaches
In this paper, we propose the application of standard decomposition approaches to find local correlations in multimodal data. In a test scenario, we apply these methods to correlate the local shape of turbine blades with their associated aerodynamic flow fields. We compare several decomposition algorithms, i.e., k-Means, Principal Component Analysis, Non-negative Matrix Factorization and Uni-or...
متن کاملBarley Productivity Decomposition in Iran: Comparison of TT, GI, MGI, and GTTI Approaches
In this paper, the authors present new indices for estimating technical change, return to scale, and TFP growth, as well as its decomposition. These indices, Modified General Index (MGI), Generalized Modified General Index (GMGI), and General Time Trend index (GTTI), are generalization of General Index approaches. These approaches were used for productivity decomposition of Iran's barely produc...
متن کاملMultiscale Analysis for Higher-order Tensors
The widespread use of multisensor technology and the emergence of big datasets have created the need to develop tools to reduce, approximate, and classify large and multimodal data such as higher-order tensors. While early approaches focused on matrix and vector based methods to represent these higher-order data, more recently it has been shown that tensor decomposition methods are better equip...
متن کاملDeep Learning Generic Features for Cross-Media Retrieval
Cross-media retrieval is an imperative approach to handle the explosive growth of multimodal data on the web. However, how to effectively uncover the correlations between multimodal data has been a barrier to successful retrieval of cross-media data. The traditional approaches learn the connection between multiple modalities by direct utilization of hand-crafted low-level heterogeneous features...
متن کامل