Search
Search Results
-
Multi-graph embedding for partial label learning
Partial label learning (PLL) is an essential weakly supervised learning method. In PLL, the example’s ground-truth label is unknown and hidden in a...
-
Targeted Label Adversarial Attack on Graph Embedding
Graph embedding is a popular technique used in various real-world applications to learn low-dimensional representations for nodes or edges in a...
-
Bi-directional matrix completion for highly incomplete multi-label learning via co-embedding predictive side information
Motivated by real-world applications such as recommendation systems and social networks where only “likes” or “friendships” are observed, we consider...
-
Multi-label classification of legal text based on label embedding and capsule network
With the development of deep learning technology and the disclosure of legal texts, the classification of legal texts has attracted the attention of...
-
Class-oriented and label embedding analysis dictionary learning for pattern classification
Analysis dictionary learning (ADL) has obtained lots of research interest in sparse representation-based classification recent years, due to its...
-
Toward embedding-based multi-label feature selection with label and feature collaboration
Similar to single-label learning, multi-label learning employs feature selection technique to alleviate the curse of dimensionality. Many multi-label...
-
Robust semi-supervised discriminant embedding method with soft label in kernel space
Considering some problems of local linear embedding methods in semi-supervised scenarios, a robust scheme for generating soft labels is designed and...
-
An adaptive convolution with label embedding for text classification
Convolutional neural network (CNNs) has made a breakthrough since deep learning was employed to text classification. However, the traditional CNNs...
-
A Label Embedding Method via Conditional Covariance Maximization for Multi-label Classification
Multi-label classification has revealed broad applications and outstanding importance in machine learning and data mining. In the multi-label domain,...
-
A Lightweight Text Classification Model Based on Label Embedding Attentive Mechanism
This paper presents a lightweight model based on the self-attention mechanism for text classification tasks. In our model, we incorporate auxiliary...
-
Self-supervised Visual-Semantic Embedding Network Based on Local Label Optimization
Image-text retrieval has always been an important direction in the field of vision-language understanding, which is dedicated to bridging the...
-
Multi-label learning of missing labels using label-specific features: an embedded packaging method
Learning label-specific features is an effective strategy for multi-label classification. Existing multi-label classification methods for learning...
-
Impact of preprocessing and word embedding on extreme multi-label patent classification tasks
Patent classification is a necessary step in the efficient processing of patent data and ensuring convenient information access to users. To address...
-
Label Embedding Based Scoring Method for Secondary School Essays
Automatic essay scoring techniques can automatically evaluate and score essays, and they have become one of the hot issues in the application of...
-
Constrained clustering with weak label prior
Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its...
-
Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations
In multi-label classification scenarios, the labels have both interactive correlations and their own respective characteristics. It is a meaningful...
-
Infinite Label Selection Method for Mutil-label Classification
Mutil-label classification is a machine learning task on a large number of labels where an instance may be associated with multiple class labels...
-
Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification
Multi-label classification handles scenarios where an instance can be annotated with multiple non-exclusive but semantically related labels...
-
HWNet v3: a joint embedding framework for recognition and retrieval of handwritten text
Learning an efficient label embedding framework for word images enables effective word spotting of handwritten documents. In this work, we propose...
-
Label-Embedding Bi-directional Attentive Model for Multi-label Text Classification
Multi-label text classification is a critical task in natural language processing field. As the latest language representation model, BERT obtains...
