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This note suggests some investigations about the complexity of multivariate problems based on quantized information rather than standard information. The extreme case of binary information is studied on two classical examples: the integration of multivariate Lipschitz functions, for which it is shown that adaptivity of the quantization process is beneficial, and the integration of multivariate trigonometric...
The High Efficiency Video Coding (HEVC) standard significantly saves coding bit-rate over the proceeding H.264 standard, but at the expense of extremely high encoding complexity. In fact, the coding tree unit (CTU) partition consumes a large proportion of HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, we propose in this paper a complexity...
Kernel based learning is very popular in machine learning but often quite costly with at least quadratic runtime complexity. Random Fourier features and related techniques have been proposed to provide an explicit kernel expansion such that standard techniques with low runtime and memory complexity can be used. This strategy leads to rather high dimensional datasets which is a drawback in many cases...
During categorization of text documents, term weighting assigns appropriate weights to different terms. All the terms having equal weights have different contribution in deciding context of the document. This paper proposes a novel concept of associating positional context among regions for term weighting. For this, Dynamic Partitioning of text documents with First and Last Partitions (DynaPart-FiLa)...
Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of seizures in EEG signs. Towards this aim, nonlinear tools are strongly recommended to select the seizure-sensitive features prior to classification. However, the choice of the feature...
One-bit transform (1BT) and its improved versions such as Constrained-1BT (C-1BT), Weighted Constrained (WC-1BT) invariably utilize filtered versions of image frames in constructing a bit plane. The proposed algorithm extends the use of filter kernel to conventional Two-bit transform (2BT) technique. The difference in the intensity levels of original frame and its filtered version represents the fluctuation...
Very often, the only reliable information available to perform change detection is the description of some “unchanged” regions. Since, sometimes, these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform semi-supervised novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved...
Streaming data are any data that are sequentially presented to a system such that future data cannot be accessed. By their nature, streaming data are often large data sets and can quickly outgrow the working memory for a typical computer. Clustering is one of the primary tasks used in the pattern recognition and data mining communities and kernel k-means is a well-studied and popular algorithm. However,...
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