Specifically, CSNMF adopts a correntropy based loss function instead of the squared Euclidean distance (SED) in constrained NMF to suppress the influence of non-Gaussian noise or outliers contaminated in real world data, and simultaneously uses two types of supervised information, i.e., the ...
where Ω(w,ws)Ω(w,ws) is the squared-Euclidean distance between the source and the target weights. The gradient of L(w)L(w) is ∇J(w)+λ×∇Ω(w,ws)∇J(w)+λ×∇Ω(w,ws). They found that NT may happen when the angle between the gradient direction of the regularizatio...
For enhancing the discriminant and parts-based interpretability, this work proposes a label and orthogonality regularized NMF (LONMF) algorithm based on the squared Euclidean distance. LONMF takes into account the label consistence with the low -dimensional projected data and orthogonal property of the...
The objective in NMF is to minimize the squared distance of A-XY with respect to X and Y, noting that X and Y must be greater or equal to 0. In other words, the objective is to minimize the following function: (3.18)‖A−XY‖2 Note that Euclidean or Frobenius (will be discussed...
RMSE: root mean squared error on held-out observations, dim: number of latent dimensions or components, FA: factor analysis, RSF: real-valued spatial factorization, PNMF: probabilistic nonnegative matrix factorization, NSF: nonnegative spatial factorization, NSFH: NSF hybrid model, lik: likelihood...
This means that the lighter flavour-10¯ scalar diquark correlations are typically favoured in all J=1/2 baryon amplitudes because the Faddeev equations involve the diquark-amplitude-squared. As will become apparent, under certain circumstances, e.g. in baryons whose valence-quarks have widely...
whereSSEis the acronym for “sum of squared errors”,Cjdenotes the centroid of thejth cluster,Pijdenotes theith pattern of thejth cluster,njdenotes the number of objects in thejth cluster,Kdenotes the predetermined number of clusters, and||·||denotes the Euclidian distance. ...
gen_features = tf.constant(np.array(gen_features), dtype=tf.float32)# Get Nearest Neighbors for all generated images.gen_real_distances = tf.sqrt(tf.abs(euclidean_distance(gen_features, real_features))) neg = tf.negative(gen_real_distances) ...
Using this model, we have developed an effective learning algorithm based on the multiplicative adaptation of the reconstruction error function defined by the squared Euclidean distance. The proposed algorithm is applied to the separation of music audio objects in the magnitude spectrum domain. ...
Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilised DLAP to analyse subtyp