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.
Results
Showing 1 to 20 of 29 for “"Metric learning"”.
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Riemannian Metric Learning via Optimal Transport
We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using …
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Bayesian distance metric learning on i-vector for speaker verification
This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an …
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A tale of two time series methods : representation learning for improved distance and risk metrics
… thesis, we present methods in representation learning for time series in two areas: metric learning and risk stratification. We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy, a simple and scalable distance metric …
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Self-supervised Representation Learning in Computer Vision and Reinforcement Learning
… is devoted to self-supervised representation learning (SSL). We consider both contrastive and non-contrastive methods and present a new loss function for SSL based on feature whitening. Our solution is conceptually simple and competitive with other methods. Self-supervised representations are …
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Spatial correlation tensor and query-augmented active clustering
… a breast cancer study, we develop a new tensor learning approach to utilize pixel-wise correlation information, which is represented through the higher-order correlation tensor. We propose novel semi-symmetric correlation tensor decomposition method which effectively captures the informative …
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Optimising image feature zero-shot-learning with EEG
… present during training, i.e. we apply zero-shot learning.<br/><br/>To achieve this goal, we first investigate a discriminative feature extraction process for EEG brain data and establish a baseline evaluation of exemplar and category-level decoding. Once competitive decoding rates are achieved, …
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Interpretable analysis of motion data
… datasets, it is necessary to design machine learning methods to analyze motions regarding their underlying characteristics systematically. Although many approaches have been suggested for motion analysis ranging from component analysis methods to deep learning algorithms, the majority of the …
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Investigation of new learning methods for visual recognition
… dissertation therefore focuses on developing new learning methods for visual recognition. Based on the conventional sparse representation, which shows its robustness for visual recognition problems, a series of new methods is proposed. Specifically, first, a new locally linear K nearest neighbor …
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Visual detection and recognition using local features
… recognition task, we adopt a state–of–the–art metric learning method and modify it to handle unknown identities. Lastly, the computational improvements achieved through leveraging par- allelism are brought together by the Vision Video Library (ViVid), which we release as open source to the …
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Understanding the rich world of outfits: a study of fashion compatibility, latent style, and outfit behavior
… item compatibility, are being learned. We take a metric learning approach to representing compatibility between pairs of items. First, we introduce a model that learns compatibility relationships in dedicated embedding subspaces dependent on item type, which results in significant gains on …
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Identification of Data Structure with Machine Learning: From Fisher to Bayesian networks
… the structure of a dataset in terms of a) metric, b) density and c) feature associations. To look into the first aspect, Fisher's metric learning algorithms are the foundations of a novel manifold based on the information and complexity of a classification model. When looking at the density …
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Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations
… Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require many such constraints before …
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Graph Embedding and Nonlinear Dimensionality Reduction
… large-scale network visualization, and metric learning for link prediction. This thesis posits that simply preserving pairwise distances, as with many spectral methods, is insufficient for capturing the structure of many datasets and that preserving both local distances and graph …
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One-vector representations of stochastic signals for pattern recognition
… naturally allows for optimal distance metric learning from the data, which generally accounts for significant performance increases in many pattern recognition tasks. This is motivated and demonstrated by our work on semi-supervised speaker clustering, where a speech utterance is …
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Sculpting representations for deep learning
In machine learning, the choice of space in which to represent our data is of vital importance to their effective and efficient analysis. In this thesis, we develop approaches to address a number of problems in representation learning. We employ deep learning as means of sculpting our …
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Efficient mining of informative descriptors from data with scarce annotations
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01
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Learning sparse features and metric in signal and image processing
This dissertation studies two aspects of feature learning: representation learning and metric in feature space, from a machine learning perspective. Feature learning is a fundamental problem in computer vision and machine learning. First introduced in computational neuroscience in the context of …
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Reconstructing neurons from serial section electron microscopy images
… images. In this thesis, we develop a set of deep learning algorithms based on convolutional nets for automated reconstruction of neurons, with particular focus on highly anisotropic images of brain tissue acquired by serial section EM (ssEM). In the first part of the thesis, we propose a …
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Deep learning of proteomics data
… highly complex data with conventional machine learning algorithms can be troublesome as these techniques require a considerable amount of feature engineering. Fortunately, a subfield of machine learning known as deep learning has recently, shown evidence towards overcoming these issues. Such …
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Automated Image Interpretation for Science Autonomy in Robotic Planetary Exploration
… exemplar scenes, it applies Mahalanobis distance metric learning (in particular, Multiclass Linear Discriminant Analysis) to discover the linear transformation of the feature space which best separates the geological classes. With the learned representation applied, a vector clustering technique …
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