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 23 for “"random projection"”.
-
Random Projection Optimal Trees Ensemble
… effect of feature selection on three methods: Random Forest (RF), Optimal Trees Ensemble (OTE) and Random Projection Ensembles (RP) in high dimensional settings. To this end, LASSO has been considered for selecting the most important features based on training data for dimension reduction. …
-
Random projection methods for stochastic convex minimization
… closed and convex sets. The problem has random features. Gradient or subgradient of objective function carries stochastic errors. Number of constraint sets can be extensive or infinitely many. Constraint sets might not be known apriori yet revealed through random realizations or randomly …
-
RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING
Hyperspectral imaging provides researchers with abundant information with which to study the characteristics of objects in a scene. Processing the massive hyperspectral imagery datasets in a way that efficiently provides useful information becomes an important issue. In this thesis, we consider …
-
Focused polynomials, random projections and approximation algorithms for polynomial optimization over the sphere
… These polynomials can be well approximated by a random projection, reducing the problem to optimization over a sphere of a much smaller dimension. We then introduce polynomials generated from a focused cone, which generalizes focused polynomials, and show that the dimension required for the …
-
On Dimensionality Reduction of Data
<p>Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss …
-
Optimization over networks: Efficient algorithms and analysis
… set. We propose gradient descent algorithms with random projections which use various communication protocols. First, we present a distributed random projection (DRP) algorithm whereby each agent exchanges local information only with its immediate neighbors at each iteration. With reasonable …
-
Dimensionality reduction for k-means clustering
… techniques such as principal component analysis, random projection, and random sampling. We next present empirical evaluations of dimensionality reduction techniques to supplement our theoretical results. We show that our dimensionality reduction algorithms, along with heuristics based on these …
-
Learning with high dimensional data and preprocessing in non-stationary environments
… complexity of high dimensional data streams, the Random Projection technique is analyzed in non-stationary envi- ronments. It is shown, that the Johnson-Lindenstrauss Lemma also holds for stream classification tasks. Further, performance comparisons of different classifiers on the projected and …
-
Code Similarity Search in a Latent Space
… codes. Hence, I explore the idea of using projection matrix to the reduce dimensionality of the problem. One approach is to use random projection. The other approach that I explore is learning the projection matrix by developing a machine learning algorithm that is supervised using the …
-
New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection
… the computer science community. Sketching uses random projection to compress the original large dataset, producing a smaller surrogate dataset that is less computationally demanding to work with. The sketched dataset can be used for approximate inference. We test our regression algorithms on a …
-
Unsupervised discovery of activity primitives from multivariate sensor data
… tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in …
-
A Method for Clustering High-Dimensional Data Using 1D Random Projections
… clustering method using a binary tree of 1D random projections. As real data tends to have a lot of structures, we show that a 1D random projection of real data captures some of that structure with a high probability. More specifically, the structure manifests itself as a clear binary …
-
Analysis of Colorectal Polyps in Optical Projection Tomography
Optical projection tomography enables 3-D imaging of colorectal polyps at resolutions of 5 – 10 μm. This thesis presents image analysis methods for the polyp diagnosis from such images. Specifically, we investigate 3-D texture-based recognition methods, as well as weakly supervised classification …
-
Inference of electromagnetic system behavior in the presence of variability
… control variables are used to account for the randomness external to a subsystem. The conditional variational autoencoder based generative model demonstrates an advantage in terms of generation accuracy as compared to its standard version, which ignores the dependency between subsystems. The …
-
Compressive gait biometric with wireless distributed pyroelectric sensors
… individuals walking along the same path, or just randomly inside a room, with an identification rate higher than 80% for around 10 subjects. For the human recognition system, innovations and adaptations are developed in: (1) sampling structure, multiple modified two-column sensor nodes are engaged …
-
Color image quality measures and retrieval
… a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon …
-
New approaches to modern statistical classification problems
… of applying an arbitrary base classifier on random projections of the feature vectors into a lower-dimensional space. In one special case that we study in detail, the random projections are divided into non-overlapping blocks, and within each block we select the projection yielding the …
-
Novel Fast Algorithms For Low Rank Matrix Approximation
… in matrix approximation have seen an emphasis on randomization techniques in which the goal was to create a sketch of an input matrix. This sketch, a random submatrix of an input matrix, having much fewer rows or columns, still preserves its relevant features. In one of such techniques random …
Page 1 of 2