Learning Deep Sparse Regularizers with Applications to Multi-View Clustering and Semi-Supervised Classification.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3082632
Abstract: Sparsity constrained optimization problems are common in machine learning, such as sparse coding, low-rank minimization and compressive sensing. However, most of previous studies focused on constructing various hand-crafted sparse regularizers, while little work was devoted… read more here.
Keywords: sparse regularizers; multi view; view clustering; deep sparse ... See more keywords
A new deep sparse autoencoder for community detection in complex networks
Sign Up to like & getrecommendations! Published in 2020 at "EURASIP Journal on Wireless Communications and Networking"
DOI: 10.1186/s13638-020-01706-4
Abstract: Feature dimension reduction in the community detection is an important research topic in complex networks and has attracted many research efforts in recent years. However, most of existing algorithms developed for this purpose take advantage… read more here.
Keywords: sparse autoencoder; deep sparse; community detection; complex networks ... See more keywords
History Matching of Naturally Fractured Reservoirs Using a Deep Sparse Autoencoder
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DOI: 10.2118/205340-pa
Abstract: Although researchers have applied many methods to history matching, such as Monte Carlo methods, ensemble-based methods, and optimization algorithms, history matching fractured reservoirs is still challenging. The key challenges are effectively representing the fracture network… read more here.
Keywords: fracture network; history matching; fractured reservoirs; method ... See more keywords
Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24091187
Abstract: Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible… read more here.
Keywords: neural network; network; deep sparse; sparse autoencoder ... See more keywords
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
Sign Up to like & getrecommendations! Published in 2022 at "PeerJ Computer Science"
DOI: 10.7717/peerj-cs.1040
Abstract: In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind… read more here.
Keywords: deep fake; deep sparse; fake; sparse auto ... See more keywords