Extended cabfac and Qmodel computer programs for Q-mode factor analysis of compositional data

Klovan, J. E.; Miesch, A.T.

doi:10.1016/0098-3004(76)90004-2

Cited by 124 publications

(44 citation statements)

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“…In addition to the differences in the collection of radiolarian census data the present study adds to previous work through improvements in the technique of Q-mode factor analysis [Klovan and Miesch, 1976]. These extensions to Q-mode factor analysis allow a better assessment of how well the factor analysis explains the distribution of individual radiolarian species and provide a more objective means of eliminating species from the final factor models.…”

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confidence: 65%

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Radiolarian‐based transfer functions for estimating mean surface ocean temperatures and seasonal range

Pisias¹,

Roelofs²,

Weber³

1997

Paleoceanography

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Abstract. New radiolarian-based transfer functions to estimate sea surface temperature (SST) and seasonal range are presented. The transfer functions are based on the approach originated by Imbrie and Kipp [ 1971 ]. The transfer functions differ from previous studies in the following three important ways: (1) extensions to Q-mode factor analysis provide an objective method to cull species in the very diverse radiolarian population; (2) a log transform of the relative abundance data is used to normalize the species percent data; and, (3) rather than writing equations for specific seasons, which are not independent data sets, statistically independent equations are developed to predict mean annual sea surface temperatures as well as seasonal temperature range. One hundred and seventy surface sediment samples from the Pacific Ocean are used to generate the SST and season temperature range transfer functions. All samples were counted using a standardized radiolarian taxonomy. Forty one radiolarian species were used in the final regression equation. Q-mode factor analysis of this data set identified seven assemblages. These assemblages,

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confidence: 65%

“…It was found that a number of species, generally rarer forms, were not well described by the factor analysis. These species had coefficients of determination [Klovan and Miesch, 1976] (Table 1). This assemblage is very similar to the "transition" assemblage of Moore [1978].…”

Section: Pisia$ Et Al: Radiolarian-based Transfer Functionsmentioning

confidence: 99%

Radiolarian‐based transfer functions for estimating mean surface ocean temperatures and seasonal range

Pisias¹,

Roelofs²,

Weber³

1997

Paleoceanography

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“…Imbrie's work belongs to another branch of geoscience studies wherein CG is used for the analysis of compositional data in earth science, such as mineral assemblages, grain-size distribution data, and geochemical and petrological data; see [29] for an overview. In fact, Imbrie's Q-mode analysis and the subsequent QMODEL by Klovan and Miesch [30] are conceptually identical to vertex or pure pixel search, although the methodology is different. Likewise, Full et al already considered the same simplex volume minimization principle as Craig's in the 1980s [31].…”

Section: Who Discovered Convex Geometry For Blind Unmixing?mentioning

confidence: 99%

“…Specifically, the constraints in (30), which form a data-enclosing polyhedron, are linear (and convex). However, there is still one obstacle-the objective function | ( ) | det H is nonconvex.…”

Section: Simplex Volume Minimizationmentioning

confidence: 99%

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Bioucas-Dias

Chan

et al. 2014

IEEE Signal Process. Mag.

404

322

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Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]- [7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing-blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts-discovered by early remote sensing researchers through empirical observations [3]- [7] and refined by later research-are elegant and very different from statistical independence-based BSS approaches established in

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“…1) (Miesch, 1976;Klovan and Miesch, 1976;Full et al, 1981). ) were obtained to describe the porosity of the 27 available thin sections .…”

Section: Geometrical Characterizationmentioning

confidence: 99%

Interpretation of Mercury Injection Experiments Using a Minimum Set of Porous Descriptors Derived by Quantitative Image Analysis

Anguy¹,

Belin²,

Ehrlich³

et al. 2011

Image Anal Stereol

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The porosity exposed in a series of petrographic thin sections from a sub-arkosic sandstone reservoir of the Alwyn area (North Sea) is described by 5 morphological porous descriptors, Pore-Types, obtained by quantitative image analysis procedures and pattern recognition algorithms developed by Ehrlich et al. (1991a). By combining Pore-Type data with capillary pressure curves, we obtain preliminary results showing that in the studied reservoir, the achieved Pore-Types relate better to petrophysics when they are complemented with a sedimentological information.

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Extended cabfac and Qmodel computer programs for Q-mode factor analysis of compositional data

Cited by 124 publications

References 2 publications

Radiolarian‐based transfer functions for estimating mean surface ocean temperatures and seasonal range

Radiolarian‐based transfer functions for estimating mean surface ocean temperatures and seasonal range

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Interpretation of Mercury Injection Experiments Using a Minimum Set of Porous Descriptors Derived by Quantitative Image Analysis

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