Search for dissertations about: "generalized singular value decomposition"

Showing result 1 - 5 of 8 swedish dissertations containing the words generalized singular value decomposition.

2. 1. Generalized Hebbian Algorithm for Dimensionality Reduction in Natural Language Processing

   Author : Genevieve Gorrell; Arne Jönsson; Robin Cooper; Alexander Rudnicky; Linköpings universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Generalized Hebbian Algorithm; Language Modelling; Singular Value Decomposition; Eigen Decomposition; Latent Semantic Analysis; Vector Space Models; Computational linguistics; Datorlingvistik;

   Abstract : The current surge of interest in search and comparison tasks in natural language processing has brought with it a focus on vector space approaches and vector space dimensionality reduction techniques. Presenting data as points in hyperspace provides opportunities to use a variety of welldeveloped tools pertinent to this representation. READ MORE

4. 2. Bilinear Regression and Second Order Calibration

   Author : Marie Linder; Pieter Kroonenberg; Stockholms universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; chemometrics; calibration; multivariate; hyphenated methods; matrix data; bilinear model; least squares; singular value decomposition; generalized rank annihilation; trilinear decomposition; parallel factor analysis; principal components regression; partial least squares; prediction; matematisk statistik; Mathematical Statistics;

   Abstract : We consider calibration of second-order (or "hyphenated") instruments for chemical analysis. Many such instruments generate bilinear two-way (matrix) type data for each specimen. The bilinear regression model is to be estimated from a number of specimens of known composition. READ MORE

5. 3. Principal Word Vectors

   Author : Ali Basirat; Joakim Nivre; Hinrich Schütze; Uppsala universitet; []
   Keywords : HUMANIORA; HUMANITIES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; word; context; word embedding; principal component analysis; PCA; sparse matrix; singular value decomposition; SVD; entropy;

   Abstract : Word embedding is a technique for associating the words of a language with real-valued vectors, enabling us to use algebraic methods to reason about their semantic and grammatical properties. This thesis introduces a word embedding method called principal word embedding, which makes use of principal component analysis (PCA) to train a set of word embeddings for words of a language. READ MORE

6. 4. Algorithms in data mining using matrix and tensor methods

   Author : Berkant Savas; Lars Eldén; Lieven De Lathauwer; Linköpings universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Volume; Minimization criterion; Determinant; Rank deficient matrix; Reduced rank regression; System identification; Rank reduction; Volume minimization; General algorithm; Handwritten digit classification; Tensors; Higher order singular value decomposition; Tensor approximation; Least squares; Tucker model; Multilinear algebra; Notation; Contraction; Tensor matricization; Newton s method; Grassmann manifolds; Product manifolds; Quasi-Newton algorithms; BFGS and L-BFGS; Symmetric tensor approximation; Local intrinsic coordinates; Global embedded coordinates; ; Numerical analysis; Numerisk analys;

   Abstract : In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes. Data mining is a general concept involving different tools for performing this kind of analysis. READ MORE

7. 5. Statistical methods for biomarker discovery in proteomics

   Author : Chuen Seng Tan; Karolinska Institutet; Karolinska Institutet; []
   Keywords : Proteomics; mass spectrometry; SELDI; MALDI; peak detection; signal detection; peak annotation; transcriptomics; data integration; maximum covariance analysis; generalized singular value decomposition;

   Abstract : Surface-Enhanced Laser Desorption and Ionization (SELDI) is a promising proteomic technique for discovering biomarkers. However, the pre-processing of the raw data is still problematic. READ MORE