Nonlinear classification of hyperspectral signatures in high noise

Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmodial data. The package categorizes data into one of three categories, "no signal", "sigmodial", or "double sigmoidal", by rigorously fitting a series of mathematical models to the data. The data is labeled as "ambiguous" if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as "ambiguous" rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.

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Some open problems in the nonlinear classification of Banach spaces

Lecture Notes in Mathematics - Banach Space Theory and its Applications ◽

10.1007/bfb0061560 ◽

1983 ◽

pp. 91-95

Author(s):

S. Heinrich ◽

P. Mankiewicz

Keyword(s):

Banach Spaces ◽

Open Problems ◽

Nonlinear Classification

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Nonlinear classification of multispectral imagery using representation-based classifiers

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8128060 ◽

2017 ◽

Author(s):

Yan Xu ◽

Qian Du ◽

Wei Li ◽

Chen Chen ◽

Nicolas H. Younan

Keyword(s):

Multispectral Imagery ◽

Nonlinear Classification

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Adaptive Metric Learning Vector Quantization for Ordinal Classification

Neural Computation ◽

10.1162/neco_a_00358 ◽

2012 ◽

Vol 24 (11) ◽

pp. 2825-2851 ◽

Cited By ~ 12

Author(s):

Shereen Fouad ◽

Peter Tino

Keyword(s):

Vector Quantization ◽

Regression Models ◽

Detrimental Effect ◽

Pattern Analysis ◽

Metric Learning ◽

Computational Cost ◽

Learning Vector Quantization ◽

Nonlinear Classification ◽

Nominal Classification

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.

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Sicegar: R package for sigmoidal and double-sigmoidal curve fitting

PeerJ ◽

10.7717/peerj.4251 ◽

2018 ◽

Vol 6 ◽

pp. e4251 ◽

Cited By ~ 10

Author(s):

M. Umut Caglar ◽

Ashley I. Teufel ◽

Claus O. Wilke

Keyword(s):

Mathematical Models ◽

Curve Fitting ◽

R Package ◽

Model Fit ◽

Noise Levels ◽

High Noise ◽

Extensive Documentation ◽

Sigmoidal Curve ◽

Sigmoidal Model

Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmoidal data. The package categorizes data into one of three categories, “no signal,” “sigmoidal,” or “double-sigmoidal,” by rigorously fitting a series of mathematical models to the data. The data is labeled as “ambiguous” if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as “ambiguous” rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.

Download Full-text