Semi-supervised feature extraction using independent factor analysis

International audienceDimensionality reduction can be efficiently achieved by generative latent variable models such as probabilistic principal component analysis (PPCA) or independent component analysis (ICA), aiming to extract a reduced set of variables (latent variables) from the original ones. In most cases, the learning of these methods is achieved within the unsupervised framework where only unlabeled samples are used. In this paper we investigate the possibility of estimating independent factor analysis model (IFA) and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. In the basic IFA model, latent variables are only recovered from their linear observed mixtures (original features). Both the mapping matrix (assumed to be linear) and the latent variable densities (that are assumed to be mutually independent and generated according to mixtures of Gaussians) are learned from observed data. We propose to learn this model within semisupervised framework where the likelihood of both labeled and unlabeled samples is maximized by a generalized expectation-maximization (GEM) algorithm. Experimental results on real data sets are provided to demonstrate the ability of our approach to find law dimensional manifold with good explanatory power

A Constrained EM Algorithm for Independent Component Analysis

We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler “soft-switching” approach is introduced, which uses only one parameter to decide on the sub- or supergaussian nature of the sources. We explain how our approach relates to independent factor analysis

Derivation of Theory by Means of Factor Analysis or Tom Swift and His Electric Factor Analysis Machine

Problems in the use of factor analysis for deriving theory are illustrated by means of an example in which the underlying factors are known. The actual underlying model is simple and it provides a perfect explanation of the data. While the factor analysis 'explains' a large proportion of the total variance, it fails to identify the known factors in the model, The illustration is used to emphasize that factor analysis, by itself, may be misleading as far as the development of theory is concerned. The use of a comprehensive, and explicit à priori analysis is proposed so that there will be independent criteria for the evaluation of the factor analytic results.factor analysis

Self-Consistent Analysis of OH-Zeeman Observations: Too Much Noise about Noise

We had recently re-analyzed in a self-consistent way OH-Zeeman observations in four molecular-cloud envelopes and we had shown that, contrary to claims by Crutcher et al., there is no evidence that the mass-to-flux ratio decreases from the envelopes to the cores of these clouds. The key difference between our data analysis and the earlier one by Crutcher et al. is the relaxation of the overly restrictive assumption made by Crutcher et al, that the magnetic field strength is independent of position in each of the four envelopes. In a more recent paper, Crutcher et al. (1) claim that our analysis is not self-consistent, in that it misses a cosine factor, and (2) present new arguments to support their contention that the magnetic-field strength is indeed independent of position in each of the four envelopes. We show that the claim of the missing cosine factor is false, that the new arguments contain even more serious problems than the Crutcher et al. original data analysis, and we present new observational evidence, independent of the OH-Zeeman data, that suggests significant variations in the magnetic-field strength in the four cloud envelopes.Comment: 8 pages, 3 figures, MNRAS in pres

PRODUCTIVITY-CONCENTRATION RELATIONSHIP IN THE U.S. MEATPACKING INDUSTRY

Previous research found a positive relationship between concentration and total factor productivity in food manufacturing. On industry (i.e., meatpacking plants [SIC 2011]) was selected for independent analysis due to a relatively sharp increase in concentration in recent years. The methodology chosen was similar to previous studies. Total factor productivity increased 2.4 percent per year, and labor productivity increased 3.3 percent per year for meatpacking plants over the 1958-82 period. Concentration in meatpacking did not positively or negatively affect total factor productivity or labor productivity over the 25-year study period.Productivity Analysis,

Iowa Cooperative Fertilizer Retail Outlets: Farmers' Attitudes and Perceptions

Based on a sample of Iowa farmers, attributes of fertilizer retail dealers are evaluated. Honest management, making deliveries on time, relative size, willingness to negotiate price, and marketing grain are the most important attributes affecting a farmer's decision to patronize an independent or cooperative dealer. Cooperative outlets are generally in a strong competitive position. The study also shows much salient information can be generated by using such statistical methods as segmentation analysis, factor analysis, and the choice model approach.Fertilizer, supply cooperative, farmers' perception, dealer attributes, segmentation analysis, factor analysis, choice model, logit, Agribusiness,