Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis
| Autor: | Ian Davidson, Jieping Ye, Xiang Li, Binbin Lin, Mojtaba Sedigh Fazli, Tianming Liu, Milad Makkie, Shannon Quinn |
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| Rok vydání: | 2016 |
| Předmět: |
Human Connectome Project
K-SVD business.industry Computer science Rank (computer programming) Big data Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Set (abstract data type) Data set 03 medical and health sciences 0302 clinical medicine Scalability Spark (mathematics) Artificial intelligence Data mining business Neural coding computer 030217 neurology & neurosurgery |
| Zdroj: | KDD |
| Popis: | It has been shown from various functional neuroimaging studies that sparsity-regularized dictionary learning could achieve superior performance in decomposing comprehensive and neuroscientifically meaningful functional networks from massive fMRI signals. However, the computational cost for solving the dictionary learning problem has been known to be very demanding, especially when dealing with large-scale data sets. Thus in this work, we propose a novel distributed rank-1 dictionary learning (D-r1DL) model and apply it for fMRI big data analysis. The model estimates one rank-1 basis vector with sparsity constraint on its loading coefficient from the input data at each learning step through alternating least squares updates. By iteratively learning the rank-1 basis and deflating the input data at each step, the model is then capable of decomposing the whole set of functional networks. We implement and parallelize the rank-1 dictionary learning algorithm using Spark engine and deployed the resilient distributed dataset (RDDs) abstracts for the data distribution and operations. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed D-r1DL model is efficient and scalable towards fMRI big data analytics, thus enabling data-driven neuroscientific discovery from massive fMRI big data in the future. |
| Databáze: | OpenAIRE |
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