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Found 9 papers, 0 papers with code
GMM Discriminant Analysis with Noisy Label for Each Class
Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance.
Online Deep Learning based on Auto-Encoder
And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure; (3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes.
Multi-View representation learning in Multi-Task Scene
By this way, the original multi-task multi-view data has degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm.
Auto-Encoder based Co-Training Multi-View Representation Learning
As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views.
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
But not all of this information is useful for classification tasks.
Partially latent factors based multi-view subspace learning
To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering.
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention.
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data.
Attentive Multi-View Deep Subspace Clustering Net
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism.
