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Actually projection pursuit classification model is a dimensional reduction technology, with optimizing projection index function and finding optimal projection value as its core problem.
In the final step, clusDCA computes its predictions for an uncharacterized gene by sorting the candidate functions by their proximity to the projected gene vector, based on the optimal projection.
With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor.
LDA calculates the optimal projection direction maximizing Fisher's criterion (12).
An efficient method is derived for the optimal projection matrix design with a given Gram matrix.
This paper describes how to generate optimal projection patterns to supplement general stereo camera systems.
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The aim of the new method is to find the prediction optimal projections on latent factors.
With enough efficient labeled samples, the optimal projections could be found by maximizing the between-class scatter variance meanwhile minimizing the within-class scatter variance.
According to Definition 3.1, the cosupport of x is a near-optimal projection.
Borrowing the idea from ASP, a theoretical study of the proposed algorithm has been performed to give guarantees for stable recovery under the assumption of the Ω - RIP and the existence of an optimal or a near-optimal projection.
A cosupport Ŝ l which implies a near-optimal projection is defined as hat{S}_{l} text{textbf{textit{v}}})={underset{varLambdain mathrm{L}_{l}}{text{argmin}}}|v-text{textbf{textit{Q}}}_{varLambda}text{textbf{textit{v}}}|^{2}_{2} (9 where v ∈ ℜ d and ( mathrm{L}_{l}=left{varLambdainleft[1,2,cdots,pright],midvarLambdamidgeq l right} ) l-cosparse cosupports.
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Justyna Jupowicz-Kozak
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