Hole or Grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Hole or Grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions

Laa, Ursula - Nama Orang; Buja, Andreas - Nama Orang; Cook, Dianne - Nama Orang; Valencia, German - Nama Orang;

Multivariate data is often visualized using linear projections, produced by techniques such as principal component analysis, linear discriminant analysis, and projection pursuit. A problem with projections is that they obscure low and high density regions near the center of the distribution. Sections, or slices, can help to reveal them. This article develops a section pursuit method, building on the extensive work in projection pursuit, to search for interesting slices of the data. Linear projections are used to define sections of the parameter space, and to calculate interestingness by comparing the distribution of observations, inside and outside a section. By optimizing this index, it is possible to reveal features such as holes (low density) or grains (high density). The optimization is incorporated into a guided tour so that the search for structure can be dynamic. The approach can be useful for problems when data distributions depart from uniform or normal, as in visually exploring nonlinear manifolds, and functions in multivariate space. Two applications of section pursuit are shown: exploring decision boundaries from classification models, and exploring subspaces induced by complex inequality conditions from a multiple parameter model. The new methods are available in R, in the tourr package. Supplementary materials for this article are available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
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Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.31 No.3 September 2022
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 739-752
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: STATISTICAL GRAPHICS
DIMENSION REDUCTION
DATA VISUALIZATION
EXPLORATORY DATA ANALYSIS
MULTIVARIATE DATA
DATA SCIENCE
PROJECTION PURSUIT
Info Detail Spesifik: https://doi.org/10.1080/10618600.2022.2035230
Pernyataan Tanggungjawab: Ursula Laa, Dianne Cook, Andreas Buja, German Valencia

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