• Smart Multi-task BregmanClustering and Multi-task Kernel Clustering
• multi-task

2019-03-08 18:02:35
Multi-task Learning Using Convolution-based Developmental Network PPT
• Multi-modal Multi-task Learning for Automatic Dietary Assessment.
• 1.multi-class, multi-label, multi-task classfication区别 每个标签有几个可能取值 几个标签 2 >2 1 binary classification multi-class classification >1 multi-label ...

1. multi-class, multi-label, multi-task classfication区别

 每个标签有几个可能取值 几个标签 2 >2 1 binary classification multi-class classification >1 multi-label classification multi-task classification

2. 例子

 binary classification: 是不是食物 是食物: (1) multi-class classification: 是食物？日用品？书本？ 是书本: (2) 或 (one hot: 0, 0, 1) multi-label classification: 是食物？塑料包装？甜的？ 是无包装的甜食: (1, 0, 1) multi-task classification: 是食物？塑料包装？出厂日期？ 是有包装的3月出厂的甜食: (1, 1, 2)

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• 一直很纠结Multi-class, Multi-label 以及 Multi-task 各自的区别和联系，最近找到了以下的说明资料： Multiclass classification means a classification task with more than two cla...

一直很纠结Multi-class, Multi-label 以及 Multi-task 各自的区别和联系，最近找到了以下的说明资料：

• Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.

• Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

• Multioutput-multiclass classification and multi-task classification means that a single estimator has to handle several joint classification tasks. This is a generalization of the multi-label classification task, where the set of classification problem is restricted to binary classification, and of the multi-class classification task. The output format is a 2d numpy array or sparse matrix.

The set of labels can be different for each output variable. For instance a sample could be assigned “pear” for an output variable that takes possible values in a finite set of species such as “pear”, “apple”, “orange” and “green” for a second output variable that takes possible values in a finite set of colors such as “green”, “red”, “orange”, “yellow”…

This means that any classifiers handling multi-output multiclass or multi-task classification task supports the multi-label classification task as a special case. Multi-task classification is similar to the multi-output classification task with different model formulations. For more information, see the relevant estimator documentation.

可以看出：

• Multiclass classification 就是多分类问题，比如年龄预测中把人分为小孩，年轻人，青年人和老年人这四个类别。Multiclass classificationbinary classification相对应，性别预测只有男、女两个值，就属于后者。
• Multilabel classification 是多标签分类，比如一个新闻稿A可以与{政治，体育，自然}有关，就可以打上这三个标签。而新闻稿B可能只与其中的{体育，自然}相关，就只能打上这两个标签。
• Multioutput-multiclass classificationmulti-task classification 指的是同一个东西。仍然举前边的新闻稿的例子，定义一个三个元素的向量，该向量第1、2和3个元素分别对应是否（分别取值1或0）与政治、体育和自然相关。那么新闻稿A可以表示为[1,1,1]，而新闻稿B可以表示为[0,1,1]，这就可以看成是multi-task classification 问题了。 从这个例子也可以看出，Multilabel classification 是一种特殊的multi-task classification 问题。之所以说它特殊，是因为一般情况下，向量的元素可能会取多于两个值，比如同时要求预测年龄和性别，其中年龄有四个取值，而性别有两个取值。

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• Discriminative multi-task multi-view feature selection and fusion for multimedia analysis
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• Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics
• KDD202论文M2GRL_A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems ｜ 图 ｜ 推荐
• 转自：https://blog.csdn.net/golden1314521/article/details/51251252一直很纠结Multi-class, Multi-label 以及 Multi-task 各自的区别和联系，最近找到了以下的说明资料：Multiclass classification means a ...

转自：https://blog.csdn.net/golden1314521/article/details/51251252

一直很纠结Multi-class, Multi-label 以及 Multi-task 各自的区别和联系，最近找到了以下的说明资料：

• Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.

• Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

• Multioutput-multiclass classification and multi-task classification means that a single estimator has to handle several joint classification tasks. This is a generalization of the multi-label classification task, where the set of classification problem is restricted to binary classification, and of the multi-class classification task. The output format is a 2d numpy array or sparse matrix.

The set of labels can be different for each output variable. For instance a sample could be assigned “pear” for an output variable that takes possible values in a finite set of species such as “pear”, “apple”, “orange” and “green” for a second output variable that takes possible values in a finite set of colors such as “green”, “red”, “orange”, “yellow”…

This means that any classifiers handling multi-output multiclass or multi-task classification task supports the multi-label classification task as a special case. Multi-task classification is similar to the multi-output classification task with different model formulations. For more information, see the relevant estimator documentation.

可以看出：

• Multiclass classification 就是多分类问题，比如年龄预测中把人分为小孩，年轻人，青年人和老年人这四个类别。Multiclass classification 与 binary classification相对应，性别预测只有男、女两个值，就属于后者。
• Multilabel classification 是多标签分类，比如一个新闻稿A可以与{政治，体育，自然}有关，就可以打上这三个标签。而新闻稿B可能只与其中的{体育，自然}相关，就只能打上这两个标签。
• Multioutput-multiclass classification 和 multi-task classification 指的是同一个东西。仍然举前边的新闻稿的例子，定义一个三个元素的向量，该向量第1、2和3个元素分别对应是否（分别取值1或0）与政治、体育和自然相关。那么新闻稿A可以表示为[1,1,1]，而新闻稿B可以表示为[0,1,1]，这就可以看成是multi-task classification 问题了。 从这个例子也可以看出，Multilabel classification是一种特殊的multi-task classification 问题。之所以说它特殊，是因为一般情况下，向量的元素可能会取多于两个值，比如同时要求预测年龄和性别，其中年龄有四个取值，而性别有两个取值。

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• 这篇文章里只介绍CO-CLUSTERING BASED MULTI-TASK MULTI-VIEW CLUSTERING FRAMEWORK部分的内容 Multi-Task Multi-View Clustering是综合了Multi-TaskMulti-View两种方法来实现多任务学习，通过一个Common View...

这篇文章里只介绍CO-CLUSTERING BASED MULTI-TASK MULTI-VIEW CLUSTERING FRAMEWORK部分的内容

Multi-Task Multi-View Clustering是综合了Multi-Task和Multi-View两种方法来实现多任务学习，通过一个Common View筛选出样本的公共特征，利用公共特征来开展整个学习的过程，就我看来，文章里并没有提供Common View的计算方法，需要样本自己提供。

Common View的概念如上图；

CO-CLUSTERING BASED MULTI-TASK MULTI-VIEW CLUSTERING FRAMEWORK部分有以下几个部分。

1.Within-View-Task Clustering

2.Multi-View Relationship Learning

3.Multi-Task Relationship Learning

其中1是遍历每一个任务的每一个视角完成聚类，2的作用是使同一个任务下两两视角的差异之和最小，如何实现我将在后面提到。3是找出在同一个视角下相关任务的shared subspace。关于这个shared subspace，我的理解是公共样本矩阵和公共特征矩阵。

接下来是算法的详细设计和构思

1.Within-View-Task Clustering

这个的证明是利用矩阵的迹的一些性质和$U^T$*U=I,$U^T$*M=I(MT是M的转置)以及D1，D2均为常量完成的，只需要将矩阵展开相乘再变化一下就可以由(2）——>（3），有时间我把证明过程贴上。

1的主要目的是学习出合适的U、M，使(3）得以成立。

矩阵转置的迹和原矩阵的迹相等这个就不用我说了吧。还有D1,D2是对角矩阵。

2.Multi-View Relationship Learning

这个的证明过程和上面的类似，都是利用tr（）的性质变换矩阵，消去常量推导的·。他的目的是在一的基础上加入多视角方面的考虑，选择综合二者的优化后的 $U_t^{(v)}$$M_t^{(v)}$

证明：

3.Multi-Task Relationship Learning

这一个利用的原理应该是SVD，通过common view v筛选X里的共享的那些特征（feature），然后利用SVD分解求出最优的共享特征的特征向量矩阵$U^{(v)}$，还有就是和1，2一起的$M_t^{(v)}$

顺便说一下，

U：the composed of the first c eigenvectors of features.U$\in R^{d*c}$(d是特征数量，c是要聚类的样本数量)

M：the composed of the first c eigenvectors of samples.U$\in R^{n*c}$(n是样本数量，c是要聚类的样本数量)

二者分别是由X $X^T$计算出来的， $U_t^{(v)}$代表在task t&view v下U的子集。

4.The Overall Objective Function

从这里我们可以看出来，CO-CLUSTERING BASED MULTI-TASK MULTI-VIEW CLUSTERING FRAMEWORK是基于1的基础上纳入2，3中的multi-view的影响和multi-task的影响，得到需要的$M_t^{(v)}$，在它的基础上进行k-means算法，得到结果。

计算优化：

优化1

其中，Definition1是找一个与目标矩阵最近似乎的矩阵X，满足$X^TX$=I (X不是方阵，所以不是正交矩阵，但有类似的意思，我也解释不清，希望大佬赐教)。Definition·2是把St（n,p）的东西变成函数$\pi$(X)。Proposition1给我们解释$\pi$(X)的计算方法。其余的就不多说了，（10）是梯度法学习参数+$\pi$(X)。顺带一提，这个方法的设计是利用参数之间的相互关系迭代优化设计的。给前一代的$M_t^{(v)}$,计算$U_t^{(v)}$

优化2

和优化1差不多。给$M_t^{(v)}$，算$U^{(v)}$（common view v下的特征矩阵）。

优化3

这个东西要分情况讨论，当$M_t^{(v)}$中的view v是common view 并且task t属于v下的公共任务集时，$M_t^{(v)}$需对整个目标式进行求解，因为我们目标函数的三个部分函数都有它的存在，见（14）。求解方法同优化1，优化2。反之，当view v不是common view 或者view v是common view ，但task t不属于v下的公共任务集时，Multi-Task Relationship Learning这一过程的条件它不满足，只计算前两部分。见（16）。

Summary

第一次写博客，做的不好的地方还有很多，如果有什么问题，请联系我，我会尽力解决。

以上。

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• Multi-task

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