Web Multimedia Object Clustering via Information Fusion

Lu, Wei; Li, Lei; Li, Tao; Zhang, Honggang; Guo, Jun

doi:10.1109/icdar.2011.72

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…K 2 R I E ÂI Cx is the lead-field matrix which summarizes volume conduction effects in the head. 1 We will henceforth assume that the sensor error E V 2 R I E ÂI T is a matrix with entries that are identically distributed zero mean Gaussian variates. Since we are modeling only sensor noise, this has been found to be a good approximation.…”

Section: A Matrix Eeg Inverse Problemmentioning

confidence: 99%

“…According to this approach, estimation of G involves finding the argumentĜ that minimizes the following augmented functional: 1 Note that this is a linear operator under the quasi-static approximation for Maxwell's equations [172]. 2 Correlated or non-Gaussian error terms can easily be dealt with, but would complicate model expressions unnecessarily.…”

Section: A Matrix Eeg Inverse Problemmentioning

confidence: 99%

“…Many examples of multimodal data integration of different fields are available, such as dynamic clustering by the combination of text and image information on the web [1]; speech recognition using complementary hand gesture [127]; multisensory image fusion, that is, merging relevant information proceeding from many images that comes from multiple sensors [128]; and human emotion recognition by exploring data arising from speech, face image, and thermal image [129]. Multimodal fusion for biological data is found in [3] for integration of gene expression data with text information and in [130] for metabolomics.…”

Section: Other Fields Of Application Of Tensor Fusionmentioning

confidence: 99%

“…Recent international efforts are marshalling the use of a bewildering array of different technologies to acquire high-throughput multimodal information about real-world systems. Examples of the systems and modalities probed are the Internet [1], geophysical data [2], and the human genome [3].…”

Section: Introductionmentioning

confidence: 99%

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Tensor Analysis and Fusion of Multimodal Brain Images

et al. 2015

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Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

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Section: A Matrix Eeg Inverse Problemmentioning

confidence: 99%

Section: A Matrix Eeg Inverse Problemmentioning

confidence: 99%

Section: Other Fields Of Application Of Tensor Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tensor Analysis and Fusion of Multimodal Brain Images

et al. 2015

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“…With the progress of powerful sensors and computer technologies, unprecedented massive data (multi‐dimensional data) are exponentially grown in many areas, such as Internet [1], geophysical data [2] and human genome [3] and so on. The processing technologies of these data have become a hot research topic, especially, dimensionality reduction (DR) is one of the most important technology [4].…”

Section: Introductionmentioning

confidence: 99%

Tensor dimensionality reduction via mode product and HSIC

Niu

2021

IET Image Processing

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Tensor dimensionality reduction (TDR) is a hot research topic in machine learning, which learns data representations by preserving the original data structure while avoiding convert samples into vectors and solving the problem of the curse of dimensionality of tensor data. In the work, a novel TDR approach based on mode product and Hilbert-Schmidt Independence criterion (HSIC) is proposed. The contributions of authors' work is described as following: (1) HSIC measures the statistical correlation of two random variables. However, instead of measuring the statistical correlation of two random variables directly, HSIC first transforms the two random variables into two reproducing kernel Hilbert spaces (RKHSs), and then measures the statistical correlation of transformed random variables by using Hilbert-Schmidt operators between the two RKHSs. The exploitation of RKHS increases the flexibility and applicability of HSIC. Although HSIC is widely used in machine learning, the authors have not seen its application to dimensionality reduction (DR)(except for authors' previous work). (2) A novel HSIC-based TDR approach is proposed, which first applies HSIC to capture statistical information of tensor data set for DR. The authors give the mathematical derivation of HSIC for tensor data and establish a framework of TDR based on HSIC, named HSIC-TDR for short, which aims to improve the DR results of tensor by exploring and preserving the statistical information of original data set. (3) Furthermore, to solve the out-of-sample problem, the authors learn an explicit expression between the dimensionality-reduced tensors and the higher-dimensional tensors by introducing mode product to HSIC-TDR. The experimental results between the proposed method and other state-of-the-art algorithm on various datasets demonstrate the well performance of the proposed method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A one-shot domain-independent robust multimedia clustering methodology based on hybrid multimodal fusion

Sevillano

Álías

2013

Multimed Tools Appl

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Web Multimedia Object Clustering via Information Fusion

Cited by 5 publications

References 13 publications

Tensor Analysis and Fusion of Multimodal Brain Images

Tensor Analysis and Fusion of Multimodal Brain Images

Tensor dimensionality reduction via mode product and HSIC

A one-shot domain-independent robust multimedia clustering methodology based on hybrid multimodal fusion

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