Results 271 to 280 of about 1,010,511 (312)

A least squares formulation for canonical correlation analysis

Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional ...
Liang Sun 0001, Shuiwang Ji, Jieping Ye
openaire   +1 more source

NOHARM: Least Squares Item Factor Analysis

Multivariate Behavioral Research, 1988
(1988). NOHARM: Least Squares Item Factor Analysis. Multivariate Behavioral Research: Vol. 23, No. 2, pp. 267-269.
C, Fraser, R P, McDonald
openaire   +2 more sources

Error analysis of distributed least squares ranking

Neurocomputing, 2019
Abstract Learning theory of distributed kernel methods has attracted much attentions recently. However, the existing theory analysis is limited to the kernel regression with pointwise losses. It is not clear whether theory guarantees can be obtained for distributed kernel methods with pairwise losses.
Hong Chen 0004, Han Li, Zhibin Pan
openaire   +1 more source

Discriminant Analysis: A Least Squares Approximation View

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2006
Linear discriminant analysis (LDA) is a very important approach to selecting features in classification such as facial recognition. However it suffers from the small sample size (SSS) problem where LDA cannot be solved numerically. The SSS problem occurs when the number of training samples is less than the number of dimensions, which is often the case ...
Peng Zhang 0016, Jing Peng 0001, Norbert Riedel   +2 more
openaire   +1 more source

Least squares online linear discriminant analysis

Expert Systems with Applications, 2012
Linear discriminant analysis (LDA) is one of the most widely used supervised dimensionality reduction algorithms. Standard LDA performs in batch way which needs all the data be available before learning. However, in many real world applications, data is coming continuously over time and sometimes undergoing concept drift, so it is more desirable to ...
Qing Wang, Liang Zhang 0019
openaire   +1 more source

Collinearity in Least-Squares Analysis

Journal of Chemical Education, 2011
How useful are the standard deviations per se, and how reliable are results derived from several least-squares coefficients and their associated standard deviations? When the output parameters obtained from a least-squares analysis are mutually independent, as is often assumed, they are reliable estimators of imprecision and so are the functions ...
openaire   +1 more source

Least-squares estimates in fuzzy regression analysis

European Journal of Operational Research, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chiang Kao, Chin-Lu Chyu
openaire   +1 more source

Partial Least Squares: A First‐order Analysis

Scandinavian Journal of Statistics, 1998
We compare the partial least squares (PLS) and the principal component analysis (PCA), in a general case in which the existence of a true linear regression is not assumed. We prove under mild conditions that PLS and PCA are equivalent, to within a first‐order approximation, hence providing a theoretical explanation for empirical findings reported by ...
Stoica, Petre, Söderström, Torsten
openaire   +1 more source

Least Square Incremental Linear Discriminant Analysis

2009 Ninth IEEE International Conference on Data Mining, 2009
Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which projects high-dimensional data into a low-dimensional space with the best separation of different classes. In many tasks, the data accumulates over time, and thus incremental LDA is more desirable than batch LDA. Several incremental LDA algorithms have been developed
Li-Ping Liu 0001, Yuan Jiang 0001, Zhi-Hua Zhou   +2 more
openaire   +1 more source

On the Equivalence of Linear Discriminant Analysis and Least Squares

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Linear discriminant analysis (LDA) is a popular dimensionality reduction and classification method that simultaneously maximizes between-class scatter and minimizes within-class scatter. In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices.
Kibok Lee 0003, Junmo Kim
openaire   +1 more source