Collections of cutting edge machine learning methods

Machine Learning Methods

There are soooo many different machine learning methods in different areas for different purposes.
Just want to collect some interesting ones.

Cluster Analysis 聚类分析

Feb 14, 2016. | By: QU Xiaofeng

度量学习是学习一个距离表达函数。

经典方法和代码

基于 Python 的度量学习包 metric-learn: Metric Learning in Python。该包包含优化过的 LMNN,ITML,SDML,LSML,NCA,LFDA 和 RCA 代码。 可以直接通过 pip install metric-learn 下载,python setup.py install 安装,python setup.py test 测试。代码在 github.

Survey

Liu Yang 06 年的 Survey07 年的 Overview。以及相应的工具箱

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Cluster Analysis 聚类分析

Feb 13, 2016. | By: QU Xiaofeng

度量学习是学习一个距离表达函数。

经典方法和代码

基于 Python 的度量学习包 metric-learn: Metric Learning in Python。该包包含优化过的 LMNN,ITML,SDML,LSML,NCA,LFDA 和 RCA 代码。 可以直接通过 pip install metric-learn 下载,python setup.py install 安装,python setup.py test 测试。代码在 github.

Survey

Liu Yang 06 年的 Survey07 年的 Overview。以及相应的工具箱

[Read More]

Metric Learning 度量学习

Feb 2, 2016. | By: QU Xiaofeng

度量学习是学习一个距离表达函数。

经典方法和代码

基于 Python 的度量学习包 metric-learn: Metric Learning in Python。该包包含优化过的 LMNN,ITML,SDML,LSML,NCA,LFDA 和 RCA 代码。 可以直接通过 pip install metric-learn 下载,python setup.py install 安装,python setup.py test 测试。代码在 github.

Survey

Liu Yang 06 年的 Survey07 年的 Overview。以及相应的工具箱

[Read More]

Subspace Clustering - 用稀疏子空间聚类做运动分割

Jun 4, 2015. | By: QU Xiaofeng

用子空间聚类算法来做运动分割,其中稀疏表示和低秩表示聚类比较多。

State-of-art 方法:

SSC (Sparse Subspace Clustering)

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Pixelwise Image Saliency with the pdf, the code and the dataset

May 22, 2015. | By: QU Xiaofeng

Keze Wang, Liang Lin, Jiangbo Lu, Chenglong Li, Keyang Shi. “PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence.”, to appear in IEEE Trans. on Image Processing. A shorter previous version was published in CVPR 2013.

Official site

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Time-lapse Mining from Internet Photos [SIGGRAPH 2015]

May 19, 2015. | By: QU Xiaofeng

[PDF]

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Co-saliency Detection

May 18, 2015. | By: QU Xiaofeng

Papers & Codes

Originally summarized by Huazhu Fu (Google site)

  • D. Jacobs, D. Goldman, and E. Shechtman, “Cosaliency: Where people look when comparing images,” in Proc. ACM Symp. User Inter. Softw. Technol. (UIST), 2010, pp. 219–228. [PDF]
  • Hongliang Li and King N. Ngan, “A Co-saliency Model of Image Pairs,” IEEE Transactions on Image Processing (TIP), vol. 20, no. 12, pp. 3365–3375, 2011. [PDF] [Code] [Dataset] [Project]

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