To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA ...
However, there is growing evidence that in the ultraviolet regime, such a picture has to be abandoned as the dimensionality of the spacetime in such high energies may effectively deviate from its infrared (corresponds to low energies) value [1]. Fractals are mathematical objects with a different...
while coupling parameters between them are changed (bifurcation parameters). We perform a systematic bifurcation analysis of two different networks, each one composed of three subnetworks. An excitatory sub-population has a positive impact on its own ...
Next, we introduce the Wasserstein metric to distinguish between a pair of macroscopic patterns. We call this approach the similarity measure between two macroscopic patterns. Finally, by using this similarity measure, we construct the corresponding metric space that reduces the dimensionality of the ...
Vector quantities in Squants are implemented as case classes that takes a variable parameter list of like quantities representing a set of point coordinates in Cartesian space. The SVector object is a factory for creating DoubleVectors and QuantityVectors. The dimensionality of the vector is determin...
To handle the moderately large dimensionality and continuous state variables present in this MDP, the action values for a state are being approximated by a four-dimensional tile coder. Tile coders are a computationally efficient means to representing continuous states as a binary vector with a size...
摘要:Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality. This paper proposes a joint dimensionality reduction and non-parametric density estimation framework, using a novel estimator that can explicitly ...
So, in the event that you need to translate from one to the other, here are some of the main equivalences you will encounter: Feature Covariates or independent variables Weights Parameters or coefficients Target Outcome or dependent variable One way out of this dimensionality problem is to assume...
(Methods). We found that disabling either the Hebbian or the decorrelation component led to a dimensionality of approximately 1, whereas the LPL rule with and without the predictive term resulted in higher dimensionality: ≈15 or ≈50, respectively (Fig.3d). Disabling the Hebbian term silenced ...
We define the space Vj as the sum of all ak multiplied by φ(2jx−k), where ak belongs to the set of real numbers. Additionally, we have the inclusion relationship V0⊂V1⊂⋯⊂Vj⊂⋯, where j takes on values in the set of non-negative integers. Moreover, we define ...