Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Subspace learning"”.
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Unified Discriminative Subspace Learning for Multimodality Image Analysis
… locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. These four algorithms are locally embedded analysis (LEA), discriminant simplex analysis (DSA), correlation embedding analysis (CEA), and correlation …
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms include …
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Markerless multiple-view human motion analysis using swarm optimisation and subspace learning
… particle swarm optimisation and charting, a subspace learning technique.In our first framework, we formulate, and perform, human motion tracking as a multi-dimensional non-linear optimisation problem, solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm. PSO …
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Recognition, Mining, Synthesis and Estimation (Rmse) for Large-Scale Visual Data Using Multilinear Models
… In video mining, we propose a novel incremental subspace learning algorithm to handle large scale or streaming tensor data. The algorithm updates the subspace matrices with sequential loading of data to reduce computational and storage complexities. We apply our algorithm to human action …
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Effective Features and Machine Learning Methods for Human Recognition Based on Multi-biometric Systems
… aims to identify effective features and machine learning methods for human recognition based on multiple biometrics and produce the sufficient combination of single biometric systems suitable in specific applications for identification purposes. For example, banking systems which use …
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Machine Learning and its Application in Automatic Change Detection in Medical Images
… in solving the problem. We design optimal subspaces to approximate the background image in more efficient fashion. This is based on our structure principal component analysis, aiming to capture the structural similarity between scans in the context of change detection. We theoretically and …
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Learning compact hashing codes with complex objectives from multiple sources for large scale similarity search
… we solve the missing data problem by latent subspace learning from multiple sources. The hashing codes are learned by enforcing the data consistency among different sources. Thirdly, we address the problem of hashing on structured data by graph learning. A weighted graph is constructed based …
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Similarity modeling for machine learning
… is an important topic for both machine learning and computer vision. In this dissertation, we first propose a discriminative similarity learning method, then introduce two novel sparse similarity modeling methods for high dimensional data from the perspective of manifold learning and …
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Sparse modeling of high-dimensional data for learning and vision
… of high-dimensional signals for various learning and vision tasks, including image classification, single image super-resolution, compressive sensing, and graph learning. Based on the bag-of-features (BoF) image representation in a spatial pyramid, we first transform each local image …
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Advanced imaging via multiplexed sensing and compressive sensing
… recovery approach, called robust orthonormal subspace learning (ROSL). Compared with RPCA using nuclear norm, ROSL presents a novel rank measure that imposes the group sparsity under orthonormal subspace, which enables it to recover a low-rank matrix by fast sparse coding. Theoretical bounds …