[HTML][HTML] The HTM spatial pooler—A neocortical algorithm for online sparse distributed coding
Hierarchical temporal memory (HTM) provides a theoretical framework that models several
key computational principles of the neocortex. In this paper we analyze an important …
key computational principles of the neocortex. In this paper we analyze an important …
Relational-regularized discriminative sparse learning for Alzheimer's disease diagnosis
Accurate identification and understanding informative feature is important for early
Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel …
Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel …
Bootstrapping social emotion classification with semantically rich hybrid neural networks
Social emotion classification aims to predict the aggregation of emotional responses
embedded in online comments contributed by various users. Such a task is inherently …
embedded in online comments contributed by various users. Such a task is inherently …
Motor imagery EEG signal processing and classification using machine learning approach
Motor imagery (MI) signals recorded via electroencephalography (EEG) is the most
convenient basis for designing brain-computer interfaces (BCIs). As MI based BCI provides …
convenient basis for designing brain-computer interfaces (BCIs). As MI based BCI provides …
Toward optimal manifold hashing via discrete locally linear embedding
Binary code learning, also known as hashing, has received increasing attention in large-
scale visual search. By transforming high-dimensional features to binary codes, the original …
scale visual search. By transforming high-dimensional features to binary codes, the original …
Neighborhood structural similarity mapping for the classification of masses in mammograms
In this paper, two novel feature extraction methods, using neighborhood structural similarity
(NSS), are proposed for the characterization of mammographic masses as benign or …
(NSS), are proposed for the characterization of mammographic masses as benign or …
Robust Adaptive Lasso method for parameter's estimation and variable selection in high-dimensional sparse models
High dimensional data are commonly encountered in various scientific fields and pose great
challenges to modern statistical analysis. To address this issue different penalized …
challenges to modern statistical analysis. To address this issue different penalized …
Robust auto-weighted multi-view subspace clustering with common subspace representation matrix
W Zhuge, C Hou, Y Jiao, J Yue, H Tao, D Yi - PloS one, 2017 - journals.plos.org
In many computer vision and machine learning applications, the data sets distribute on
certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the …
certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the …
Logistic localized modeling of the sample space for feature selection and classification
N Armanfard, JP Reilly… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Conventional feature selection algorithms assign a single common feature set to all regions
of the sample space. In contrast, this paper proposes a novel algorithm for localized feature …
of the sample space. In contrast, this paper proposes a novel algorithm for localized feature …
Improving the impervious surface estimation from hyperspectral images using a spectral-spatial feature sparse representation and post-processing approach
S Liu, G Gu - Remote Sensing, 2017 - mdpi.com
Impervious surfaces have been widely recognized as an indicator for urbanization and
environment monitoring. Plenty of methods have been proposed to extract impervious …
environment monitoring. Plenty of methods have been proposed to extract impervious …