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A correlation analysis framework via joint sample and feature selection
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DOI：10.1007/s11042-022-14237-5.png)
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摘要
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Correlation Analysis is a popular technique for describing relationships between two datasets. In this paper, we proposed a correlation analysis framework via Joint Sample and Feature Selection (CAF-JSFS). Different from traditional correlation analysis where only feature selection is considered and each data point is treated equally, the significance of each data point is measured by a sample selection strategy in this framework. Considering that the principal projection is a feasible representation of data, the relationship between this principal projection and each data sample is recursively learnt through two sample selection strategies: cosine similarity and total distance metrics. In addition, CAF-JSFS solves the problem of feature redundancy caused by sample feature selection, and eliminates irrelevant features, thereby improving classification accuracy. This enhances the discriminative power of CAF-JSFS in noisy scenarios which makes better correlation projections achievable to improve performance. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to the state-of-the-art methods.
Keyword:
Correlation analysis
Sample selection
Feature selection
Cosine similarity metric
Total distance metric
期刊
IF:
3
论文数:
1.9W
被引数:
3.2W
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