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Nonlinear dimensionality reduction by John A. Lee, Michel Verleysen

By John A. Lee, Michel Verleysen

This booklet describes demonstrated and complicated equipment for decreasing the dimensionality of numerical databases. each one description starts off from intuitive rules, develops the mandatory mathematical information, and ends via outlining the algorithmic implementation. The textual content presents a lucid precis of evidence and ideas when it comes to famous equipment in addition to fresh advancements in nonlinear dimensionality relief. tools are all defined from a unifying viewpoint, which is helping to spotlight their respective strengths and shortcomings. The presentation will attract statisticians, laptop scientists and knowledge analysts, and different practitioners having a uncomplicated heritage in facts or computational learning.

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This means that discovering the dependency between that variable and the other ones can be difficult. Therefore, that variable should intuitively be processed exactly as in the previous case, that is, either by discarding it or by avoiding the standardization. The latter could only amplify the noise. By definition, noise is independent from all other variables and, consequently, PCA will regard the standardized variable as an important one, while the same variable would have been a minor one without standardization.

Extending PCA to nonlinear models still remains an appealing challenge. Chapters 4 and 5 deal with pure dimensionality reduction. Without the necessicity of retrieving exactly the latent variables, more freedom is left and numerous models become possible. 5 Toward a categorization of DR methods 37 PCA embedding 4 x2 2 0 −2 −4 −8 −6 −4 −2 0 2 4 6 8 x 1 Fig. 8. Projection of the three-dimensional observations (second plot of Fig. 7) onto the first two principal components found by PCA. The solid line shows a schematic representation of the true latent distribution, whereas the dashed one corresponds to the estimated latent variables.

These implementations do not offer the same strong guarantees as the algebraic versions, but may be very useful in real-time application, where computation time and memory space are limited. When estimating the latent variables, it must be pointed out that Eq. 19) is not very efficient. Instead, it is much better to directly remove the unnecessary columns in V and to multiply by y afterwards, without the factor IP ×D . 39) = IP ×D ΣUT . 40) ˆ consists of copying the first As Σ is diagonal, the cheapest way to compute X P columns of U, multiplying them by the corresponding diagonal entry of Σ, and, finally, transposing the result.

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