Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Most learning approaches treat dimensionality reduction (DR) and clustering separately (ie,
sequentially), but recent research has shown that optimizing the two tasks jointly can …
sequentially), but recent research has shown that optimizing the two tasks jointly can …
A novel double-stacked autoencoder for power transformers DGA signals with an imbalanced data structure
D Yang, J Qin, Y Pang, T Huang - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Artificial intelligence is the general trend in the field of power equipment fault diagnosis.
However, limited by operation characteristics and data defects, the application of the …
However, limited by operation characteristics and data defects, the application of the …
MUSENET: Multi-scenario learning for repeat-aware personalized recommendation
Personalized recommendation has been instrumental in many real applications. Despite the
great progress, the underlying multi-scenario characteristics (eg, users may behave …
great progress, the underlying multi-scenario characteristics (eg, users may behave …
Clustering with orthogonal autoencoder
Recently, clustering algorithms based on deep AutoEncoder attract lots of attention due to
their excellent clustering performance. On the other hand, the success of PCA-Kmeans and …
their excellent clustering performance. On the other hand, the success of PCA-Kmeans and …
Rail crack detection using acoustic emission technique by joint optimization noise clustering and time window feature detection
Recently, acoustic emission (AE) technology has been investigated to detect rail cracks.
However, AE signals of cracks are often submerged in heavy noises in practical application …
However, AE signals of cracks are often submerged in heavy noises in practical application …
Differentially private federated clustering over non-IID data
In this article, we investigate the federated clustering (FedC) problem, which aims to
accurately partition unlabeled data samples distributed over massive clients into finite …
accurately partition unlabeled data samples distributed over massive clients into finite …
Clustering by orthogonal NMF model and non-convex penalty optimization
The non-negative matrix factorization (NMF) model with an additional orthogonality
constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found …
constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found …
Clustered Embedding Learning for Recommender Systems
In recent years, recommender systems have advanced rapidly, where embedding learning
for users and items plays a critical role. A standard method learns a unique embedding …
for users and items plays a critical role. A standard method learns a unique embedding …
Demystifying model averaging for communication-efficient federated matrix factorization
Federated learning (FL) is encountered with the challenge of training a model in massive
and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm …
and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm …
Federated matrix factorization: Algorithm design and application to data clustering
Recent demands on data privacy have called for federated learning (FL) as a new
distributed learning paradigm in massive and heterogeneous networks. Although many FL …
distributed learning paradigm in massive and heterogeneous networks. Although many FL …