国际神经系统杂志(International Journal of Neural Systems)是一份经过同行评议的双月刊,内容涉及自然和人工神经系统的信息处理。它出版这一涉及物理学、生物学、心理学、计算机科学和工程学的广泛学科的所有方面的原始贡献。贡献包括研究论文、评论、简短的通信和给编辑的信。该杂志对这一多学科领域提出了一种全新的、非教条的态度,旨在成为一个论坛,为具有计算能力的系统中的集体和合作现象提供新颖的想法和更好的理解。 官网地址:http://dblp.uni-trier.de/db/journals/ijns/

This work includes all the technical details of the Sequential Principal Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear and invertible feature extraction technique. The identified curvilinear features can be interpreted as a set of nonlinear sensors: the response of each sensor is the projection onto the corresponding feature. Moreover, it can be easily tuned for different optimization criteria; e.g. infomax, error minimization, decorrelation; by choosing the right way to measure distances along each curvilinear feature. Even though proposed in [Laparra et al. Neural Comp. 12] and shown to work in multiple modalities in [Laparra and Malo Frontiers Hum. Neuro. 15], the SPCA framework has its original roots in the nonlinear ICA algorithm in [Malo and Gutierrez Network 06]. Later on, the SPCA philosophy for nonlinear generalization of PCA originated substantially faster alternatives at the cost of introducing different constraints in the model. Namely, the Principal Polynomial Analysis (PPA) [Laparra et al. IJNS 14], and the Dimensionality Reduction via Regression (DRR) [Laparra et al. IEEE TGRS 15]. This report illustrates the reasons why we developed such family and is the appropriate technical companion for the missing details in [Laparra et al., NeCo 12, Laparra and Malo, Front.Hum.Neuro. 15]. See also the data, code and examples in the dedicated sites http://isp.uv.es/spca.html and http://isp.uv.es/after effects.html

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