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Showing 1 to 20 of 167 for “"singular value decomposition"”.
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Activity Recognition using Singular Value Decomposition
… a user's daily activities is of substantial value. It can be used to enhance medical monitoring by maintaining a diary that lists what a person was doing and for how long. The design of a wearable system to record context such as activity recognition is influenced by a combination of …
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Distributed Singular Value Decomposition Through Least Squares
Singular value decomposition (SVD) is an essential matrix factorization technique that decomposes a matrix into singular values and corresponding singular vectors that form orthonormal bases. SVD has wide-ranging applications from principal component analysis (PCA) to matrix completion and …
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Generating new data points using singular value decomposition
… for small data sets. It introduces a Single Value Decomposition (SVD)-based model that draws inspiration from the ability of SVD to estimate a lower rank matrix. This approach seeks to overcome the limitations imposed by sample size constraints by expanding available data. Motivated by …
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Curve characterization with high-order tensor singular value decomposition
Made available in DSpace on 2016-07-07T19:58:19Z (GMT). No. of bitstreams: 2 CAI-THESIS-2016.pdf: 2599852 bytes, checksum: c5150b52d2e0c26a7abcb48c71c76bf3 (MD5) LICENSE.txt: 4206 bytes, checksum: cd03b4878a87f39bc09a17838bd5b8de (MD5) Previous issue date: 2016-04-28
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Finding analogies in semantic networks using the singular value decomposition
We present CROSSBRIDGE, an algorithm for finding analogies in large, sparse semantic networks. We treat analogies as comparisons between domains of knowledge. A domain is a small semantic network, i.e., a set of concepts and binary relations between concepts. We treat our knowledge base (the large …
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A VLSI systolic array processor for complex singular value decomposition
The singular value decomposition is one example of a variety of more complex routines that are finding use in modern high performance signal processing systems. In the interest of achieving the maximum possible performance, a systolic array processor for computing the singular value decomposition …
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Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia
… through the implementation of tiled QR-based singular value decomposition (SVD), demonstrating how it streamlines the development process and accelerates scientific discovery. The developed framework is used to implement an in-GPU tiled SVD and an out-of-core GPU-accelerated SVD. Furthermore, …
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Efficient reinforcement learning via singular value decomposition, end-to-end model-based methods and reward shaping
Reinforcement learning (RL) provides a general framework for data-driven decision making. However, the very same generality that makes this approach applicable to a wide range of problems is also responsible for its well-known inefficiencies. In this thesis, we consider different properties which …
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Multiprocessor sparse SVD algorithms and applications
… develop four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger or smaller than the number of …
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Exploration, processing and visualization of physiological signals from the ICU
… a novel method for data clustering based on the singular value decomposition and present some potential applications based on this method for use within the ICU setting.
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Reconstruction de données satellitaires manquantes du lac Tanganyika
… d’une généralisation du théorème SVD (Singular Value Decomposition) des matrices aux tenseurs d’ordre trois. Les performances de ces deux algorithmes sont ensuite comparées sur les données satellitaires du lac Tanganyika fournies par Yves Cornet*, le but étant de reconstruire ces …
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Latent Semantic Indexing and Information Retrieval-A quest with BosSE
… Indexing (LSI) and an explanation of the Singular Value Decomposition (SVD) is given.
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Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects
… parameters can be estimated by computing the singular value decomposition of a 3 x 3 matrix. The solution would be unique if three distinct frames are given. For the curved surface case, it is shown that seven point correspondences are sufficient to uniquely determine from two perspective …
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Faster linear algebra for data analysis and machine learning
… analysis and machine learning. Examples include singular value decomposition and low-rank approximation, several varieties of linear regression, data clustering, and nonlinear kernel methods. To scale these problems to massive datasets, we design new algorithms based on random sampling and …
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A Study of Wireless Modem Performance Using Multiple Element Antennas
… Output (MIMO) systems. In such a case, the Singular Value Decomposition (SVD) of the channel matrix gives the optimal precoder and decoder. This thesis studies the performance of the SVD architecture under varying propagation environments, as well as its robustness to various impairments, …
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Empirical Approch For Rate Selection In MIMO OFDM
… The most dominant proposal is the use of singular value decomposition based MIMO methods to achieve the high data rate. The selection of modulation and coding rates plays a significant role in the overall throughput of the system, more so in cases where the traffic between the transmitter …
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The analysis and synthesis of efficient algorithm-based error detection schemes for hypercube multiprocessors
… Fourier Transform; (3) QR factorization; (4) singular value decomposition. We describe extensive studies of the error coverage of our system-level error detection schemes in the presence of finite precision arithmetic which affects our system-level encodings.
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Modeling of short-distance running
… linear least squares estimation based on the Singular Value Decomposition (SVD) in order to estimate the two physiological parameters. Finally, we apply this computational model to real world data, first on a 1987 World Track record, and more extensively, on larger data sets consisting of …
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