Random projection

노트

• After trying PCA, robust PCA, ICA, removing highly correlated features, I was thinking to use Random Projection.
• But, it seems that they don't support random projection directly for dimension reduction.
• Is that possible to implement random projection easily in R?
• Or, taking advantage of existing tools to do dimension reduction with Random Projection in R?
• In this paper, we propose an architecture that incorporates random projection into NEC to train with more stability.
• Recently, Random Projection (RP) has emerged as a powerful method for dimensionality reduction.
• Random projection allows one to substantially reduce dimensionality of data while still retaining a significant degree of problem structure.
• Random projections gives you a kind of probabilistic warranty against that situation.
• , in this scenario of continuously learning from large streams of data, I believe random projections are a sensible and efficient approach.
• Random projection is a powerful method to construct Lipschitz mappings to realize dimensionality reduction with a high probability.
• Random projection does not introduce a significant distortion when the dimension and cardinality of data both are large.
• In Section 7.3, the justification of the validity of random projection is presented in detail.
• The applications of random projection are given in Section 7.4.
• However, using random projections is computationally significantly less expensive than using, e.g., principal component analysis.
• We also show experimentally that using a sparse random matrix gives additional computational savings in random projection.
• Initially, high-dimensional data are projected into a lower-dimensional Euclidean space using random projections.
• the pairwise distances between data points are preserved through random projections.

메타데이터

위키데이터

• ID : Q18393452

Spacy 패턴 목록

• [{'LOWER': 'random'}, {'LEMMA': 'projection'}]