In multi-view multi-label learning, each object is represented by multiple heterogeneous data and is simultaneously associated with multiple class labels. Previous studies usually use shared subspaces to fuse multi-view representations. However, as the number of views increases, it is more challenging...
潜在语义感知的多视图多标签学习方法(Latent Semantic-aware Multi-view Multi-label Learning, 简称 LSVML)是一种用于处理包含多个特征视图和多个标签的数据集的机器学习技术。 这类方法特别适用于文本、图像和其他复杂数据类型,其中数据可以从多个角度(视图)进行描述,并且可以属于多个类别(多标签)。 核心思想 LSVML ...
Multi-view multi-label (MVML) learning is a framework for solving the problem of associating a single instance with a set of class labels in the presence o
Multi-label learning 多标签学习是多任务学习中的一种,建模多个label之间的相关性,同时对多个label进行建模,多个类别之间共享相同的数据/特征。 Multi-class learning多类别学习是多标签学习任务中的一种,对多个相互独立的类别(classes)进行建模。 【自己理解】 4. Multi-scale learning 多尺度学习: 图像摘自Spatial ...
In multi-view multi-label learning (MVML), each instance is described by several heterogeneous feature representations and associated with multiple valid labels simultaneously. Although diverse MVML methods have been proposed over the last decade, most previous studies focus on leveraging the shared sub...
Based on intuitive understanding, we propose a Two-step Multi-view and Multi-label Missing Label learning optimization solution(TM3L). The first step is to solve the multi-view learning problem by finding the data representation of the common low-dimensional space of all views through subspace ...
Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information...
Incomplete multi-view learningMissing multi-label classificationSiamese networkDynamic fusionMulti-view multi-label classification (MvMLC) has garnered significant interest because of its ability to handle complex datasets. However, the inherent complexity of real-world data often results in incomplete ...
We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting...
显而易见,“不完整的多视图弱标签学习”(Incomplete Multi-View Weak-Label Learning)是“不完整的多视图学习”与“弱标签学习”的交叉子方向。它可以看作是“多视图多标签学习”(Multi-View Multi-Label Learning)遇上了同时属于不完整的多视图和弱标签的数据的一种特殊场景。 就目前来说,这个方向下的研究仍然很...