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High Performance Computing and Real Time Software for High Dimensional Data Classification


We present high performance computing and real time software for high dimensional data classification. We investigate the OpenMP parallelization and optimization of two graph-based data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithm. We use OpenMP as the parallelization language to parallelize the most time-consuming parts, which is the Nystr\"om extension eigensolver. The Nystr\"om extension calculates eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel's Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. A large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling. We further develop the parallelized real time software. The software is published on Image Processing On Line (IPOL) and uses IPOL's server. It provides real time computation for image classification.

We apply the parallelized classification algorithms to various problems. The first one is hyperspectral image classification, a challenging modality due to the dimension of pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Our methods use the full dimensionality of the data and consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require the computation of the full graph. The parallelized Nystr\"om extension method randomly samples the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundreds eigenfunctions, we can implement the clustering method to solve variational problems for graph-cut-based semi-supervised and unsupervised classification problems. The second application is ego-motion classification of body-worn videos. Portable cameras record dynamic first-person video footage and these videos contain information on the motion of the individual on whom the camera is mounted, defined as ego. We address the task of discovering ego-motion from the video itself, without other external calibration information. We investigate the use of similarity transformations between successive video frames to extract signals reflecting ego-motions and their frequencies. We use the parallelized graph-based unsupervised and semi-supervised learning algorithms to segment the video frames into different ego-motion categories. We show very accurate results on both choreographed test videos and ego-motion videos provided by the Los Angeles Police Department.

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