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Found 8 papers, 1 papers with code
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
This work proposes an unsupervised fusion framework based on deep convolutional transform learning.
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses the problem of analyzing multi-channel time series data %.
Deep Convolutional Transform Learning -- Extended version
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL).
Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning
There are hardly any studies in deep learning based multi label classification; two new deep learning techniques to solve the said problem are fundamental contributions of this work.
Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis
In order to accelerate, compressed sensing based techniques have been proposed.
Label Consistent Transform Learning for Hyperspectral Image Classification
This work proposes a new image analysis tool called Label Consistent Transform Learning (LCTL).
Kernel Transform Learning
The concept of kernel dictionary learning has been introduced in the recent past, where the dictionary is represented as a linear combination of non-linear version of the data.
Transformed Subspace Clustering
We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.
