机器学习之聚类——从教授的等式到凸聚类


活动地址:CSDN21天学习挑战赛

引子:大佬的等式

在美国,有个牛逼的大学,叫华盛顿大学,其中有个牛逼的计算机科学教授,佩德罗·多明戈斯(Pedro Domingos),他是《终极算法》的作者,还有很多著作和头衔,不过,今天我们关注的不是这些著作和头衔,而是他写过的一个著名的方程式:

机器学习=表示+优化+评估

[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:139b2ded-bf18-4d45-aabc-8ec9ed61e2a0

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凸优化和非凸优化的区别

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凸聚类

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机器学习之聚类——从教授的等式到凸聚类

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无论是k-means算法还是谱聚类算法,本质都是一个非凸问题,这使得问题的解受初始值的影响很大。虽然人们已经提出了很多不同的初始化算法来提升聚类性能,但至今都没有一种最优的初始化策略出现。通过将聚类问题描述为一个凸优化问题,凸聚类算法通过凸优化算法获得聚类问题的唯一解,从而避免初始值的选取对聚类结果的影响。

机器学习之聚类——从教授的等式到凸聚类

上面目标函数的第一项为矩阵U与X之差的Frobenius范数,用来控制矩阵U与X的距离,第二项为矩阵U内部不同列之间距离的加权和,用来控制矩阵U内部不同列之间的距离。可以预见,能够目标函数最小的矩阵U可以使U与X接近的同时使U内列向量之间距离较小。在凸聚类算法处理后,U内部同属于一个聚类的向量距离会变小,而不属于同一个聚类的向量虽然距离也可能会变小,但是幅度不大,使得U内部的聚类模式体现得更加明显,之后只需要对矩阵U使用非常简单的聚类算法(比如k-means等),就可以获得U中列向量的聚类,它们也就对应了X中列向量的聚类。在实践中,一般使用直接比较U列向量之间距离的方法来确定聚类个数和每一个列向量所属的聚类。
凸聚类算法试图绕过初始化问题,直接针对聚类问题设计出一个凸的目标函数,从而仅仅通过非常简单的算法就能求得全局最优解。通过连续可调的参数

机器学习之聚类——从教授的等式到凸聚类,凸聚类算法可以生成数目不等的聚类,从而实现对聚类个数k的间接控制。

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Original: https://blog.csdn.net/weixin_37522117/article/details/126284749
Author: 肥猪猪爸
Title: 机器学习之聚类——从教授的等式到凸聚类

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