A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates.
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions.
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection.
Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.
Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.
The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.
Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.
One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.
A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model.