dimension reduction

Types

Dataset size reduction can be performed in one of the two ways [5]

• feature set reduction (feature selection)
• sample set reduction.

Traditional dimensionality reduction approaches:

• Principal Component Analysis (PCA)
• linear discriminant analysis (LDA)

the dimensionality of data increases, the computational cost of traditional dimensionality reduction methods grows exponentially, and the computation becomes prohibitively intractable.

These drawbacks have triggered the development

• random projection reduced time cost

However, the RP transformation matrix is generated without considering the intrinsic structure of the original data and usually leads to relatively high distortion.

Reference List

1. https://zhuanlan.zhihu.com/p/159285110?utm_id=0
2. https://zhuanlan.zhihu.com/p/62470700?utm_id=0
3. Xie, H., Li, J., & Xue, H. (2017). A survey of dimensionality reduction techniques based on random projection. arXiv preprint arXiv:1706.04371.
4. Boutsidis, C., Zouzias, A., & Drineas, P. (2010). Random projections for $k$-means clustering. Advances in neural information processing systems, 23.
5. Jović, A., Brkić, K., & Bogunović, N. (2015, May). A review of feature selection methods with applications. In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 1200-1205). Ieee.