基于SKICA的非线性过程缓变故障检测方法研究 - 百度学术
针对非线性工业过程缓变型故障的检测问题,提出一种基于累积和核独立元分析(skica)的故障检测方法.通过核函数技术将观测数据从非线性空间映射到线性空间,然后对线性空间的数据应用独立元分析算法,提取观测数据中的非线性独立元.为了更好的检测过程中微小 ...
SKICA: A feature extraction algorithm based on ... - ResearchGate
2018年7月26日 · In this paper, we propose a new discriminative method called Supervised Kernel ICA (SKICA) that uses a non-linear kernel approach combined with Supervised ICA-based local image descriptors.
In this paper we propose a supervised Kernel ICA for local image descriptors (SKICA descriptors), which combines nonlinear kernels with SICA. SKICA de-scriptors accept the input as normalised and gradient patches (the PCA-SIFT descriptor). In natural images, nearby pixels are statistically related and gradi-
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SKICA: A feature extraction algorithm based on ... - 百度学术
This article conducts experiments on artificial datasets, Cloud datasets, and KDD Cup datasets to evaluate the effectiveness of SKICA. The experimental results show that SKICA outperforms several popular supervised feature extraction algorithms, including LDA, LDA with kernel (KDA), and supervised ICA (SICA).
SKICA: A feature extraction algorithm based on supervised ICA …
Semantic Scholar extracted view of "SKICA: A feature extraction algorithm based on supervised ICA with kernel for anomaly detection" by Guiping Wang et al.
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Local Image Descriptors Using Supervised Kernel ICA
2009年9月1日 · In this paper, we propose a new discriminative method called Supervised Kernel ICA (SKICA) that uses a non-linear kernel approach combined with Supervised ICA-based local image descriptors.
SKICA: A feature extraction algorithm based on supervised ICA …
In order to cope with non-Gaussian data and implement nonlinear feature extraction, this article proposes a feature extraction algorithm based on Supervised Independent Component Analysis with Kernel (termed SKICA). SKICA first adopts Kernel Principle Component Analysis (KPCA) to whiten the datasets.
SKICA: A feature extraction algorithm based on supervised ICA …
Gui Ping Wang, Jian Xi Yang. SKICA: A feature extraction algorithm based on supervised ICA with kernel for anomaly detection. Journal of Intelligent and Fuzzy Systems, 36 (1):761-773, 2019. [doi] Abstract. Authors.
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