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stochastic gradient descent optimization algorithm  (Stiefel)

 
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    Stiefel stochastic gradient descent optimization algorithm
    Stochastic Gradient Descent Optimization Algorithm, supplied by Stiefel, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gradient-descent+algorithm/stochastic+gradient+descent+algorithm/10__1109_slash_tnnls__2025__3561930-78-22-29
    Average 90 stars, based on 1 article reviews
    stochastic gradient descent optimization algorithm - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Heterogeneous Riemannian Few-Shot Learning Network
    Article Snippet: How to learn and accurately distinguish new concepts from few samples, as humans do, is a long-standing concern in artificial intelligence (AI).. Studies in brain science and neuroscience have shown that human brain perception is based on nonlinear manifolds, and high-dimensional manifolds can facilitate concept learning in neural circuits.. Based on this inspiration, in this paper, we propose a heterogeneous Riemannian few-shot learning network (HRFL-Net), which is the first few-shot learning method to perform end-to-end deep learning on heterogeneous Riemannian manifolds.

    Article Title: Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search
    Article Snippet: Binary code learning, a.k.a. hashing, has been successfully applied to the approximate nearest neighbor search in large-scale image collections.. The key challenge lies in reducing the quantization error from the original real-valued feature space to a discrete Hamming space.. Recent advances in unsupervised hashing advocate the preservation of ranking information, which is achieved by constraining the binary code learning to be correlated with pairwise similarity.

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    Article Title: Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations.
    Article Snippet: .. Here, we developed a variant of HLDA, termed GDHLDA (gradient descent-based HLDA) which takes advantage of the strengths of ZHLDA and MHLDA approaches by integrating the harmonic mean approaches for both within-class and between-class variances and, similar to ZHLDA, uses the Stiefel gradient descent algorithm for optimization. .. Specifically, in GDHLDA a constrained trace ratio optimization problem on the Stiefel manifold is considered (see Algorithm 1), with the objective of finding an orthonormal basis set (or projection matrix) W that minimizes the objective function J, given by: J(W) = ∑ ∑ nknl C l=k+1 C−1 k=1 Tr(WTSWW) Tr(WTBklW) s. t. WTW = I. (10) Here, C is the number of classes, ni is the count of data points in class i, SW is the within-class scatter matrix, and Bkl is the pairwise between-class scatter matrix, defined as follows: SW = (∑ Σk −1 C k=1 ) −1 (12) Bkl = (μk − μl)(μk − μl) T (13) where Σk is the covariance matrix which is defined as Σk = ∑ (x − μk)(x − μk) T x∈Ck (14) where Ck is class k and μk is the mean vector of class k.



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