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A technology for collaborative training and learning methods, applied in the field of multi-view semi-supervised learning models, which can solve problems such as lack of model explanations
Inactive Publication Date: 2017-12-12
XI AN JIAOTONG UNIV
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Furthermore, it is very important for a machine learning method to have a machine learning optimization model that can explain its essence, which is also one of the basic three elements of machine learning (i.e., training data, decision function, performance measure or optimization objective) , while the traditional collaborative training methods basically lack a perfect model explanation
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Embodiment 1
[0078] Table 1 is an explanatory table for six groups of text data.
[0079] Table 1: Experimental data of Example 1
[0080]
[0081]
[0082] The six text data sets shown in Table 1 are used as the experimental objects of the present invention, and all samples are manually divided into two fields of view. Each dataset has two categories whose structural characteristics are illustrated in Table 1.
[0083] Table 2 is the accuracy table for classification using seven semi-supervised methods including the present invention on six groups of text data.
[0084]
[0085] see figure 1 , step S1: read the text data, for the first data set in Table 1, select 2 respectively from positive samples and negative samples k , 3.2 k samples are labeled samples, and the remaining samples are unlabeled samples. For the second, third, and fourth data sets in Table 1, select 2 for the positive and negative classes respectively k , 6.2 k samples are labeled samples. For the last ...
Embodiment 2
[0135] Table 3 is the accuracy table of Person re-Identification using three multi-view semi-supervised methods including the invention on the Market-1501 dataset.
[0136]
[0137] In this example, the Market-1501 dataset is used for the Person re-identification task. Personre-ID refers to a class of tasks where, for a person captured by a camera, it is necessary to determine whether the person is captured by other cameras. The Market-1501 dataset includes 32668 photos of 1501 individuals. Each person's photo is captured by a maximum of six cameras and a minimum of two cameras. Here, 12,936 clipped photos containing 751 people are selected as the training data set, and 19,732 clipped photos containing 750 people are selected as the test data set.
[0138] Next, feature extraction is performed on the training data and test data sets. In order to obtain different features, different networks such as caffenet, Googlenet, and Vggnet are used here. The features extracted by ...
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Abstract
The invention discloses a self-paced-cooperative training learning method. The method comprises the following steps of: obtaining data, from two visual fields, of a target problem, and initializing a model, wherein the data comprises a small amount of labeled data and a large amount of unlabeled data; respectively determining corresponding optimization targets on the two visual fields; embedding a self-paced regular term in a loss function of each visual field so as to realize steady learning under the visual field; associating the two visual fields through a regular term; obtaining a multi-visual field semi-supervised self-paced-cooperative training model which is embedded into a steady learning mechanism and has model interpretation; and obtaining high-quality labeling of the unlabeled data by applying the small amount of labeled data and large amount of unlabeled data in a target field and a semi-supervised multi-visual point learning model, and obtaining reliable learning devices under the two visual fields at the same time. The invention aims at providing a steady learning model with a replacement mode for the traditional cooperative training algorithms to ensure that data lack of labeling in the target field can obtain more correct and high-quality labeling.
Description
technical field [0001] The invention relates to a multi-view semi-supervised learning model and method, in particular to a novel self-paced-cooperative training model and learning method. Background technique [0002] There are a lot of real-time data on the Internet, such as news, pictures, videos, etc., but most of these data only have vague descriptions about events, and some even have no labeled information at all. When we want to perform query or classification tasks, in traditional machine learning algorithms, this part of unlabeled information or weakly labeled data is basically not used, resulting in a large loss of available information. This type of data is characterized by a large amount of unlabeled data and limited available labeled data. Therefore, how to mine information in unlabeled data has become a technology that has emerged in the field of machine learning in recent years. On the premise of making full use of labeled data, extract information from unlab...
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