[PDF] Top 20 Metric learning with convex optimization
Has 10000 "Metric learning with convex optimization" found on our website. Below are the top 20 most common "Metric learning with convex optimization".
Metric learning with convex optimization
... of S ~1 Sb corresponding to the largest eigenvalues are used to form the optimal W°. It is believed that the largest eigenvalue contains more useful information. Nevertheless such a GEVD approach cannot produce an ... See full document
79
Metric and Kernel Learning Using a Linear Transformation
... kernel learning (Lanckriet et ...non-convex optimization, or are restricted to a supervised learning ...kernel learning (Zhu et al., 2005), manifold-based kernel learning (Bengio ... See full document
29
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
... There have been some attempts to apply (L)BFGS directly to nonsmooth optimization problems, in the hope that they would perform well on nonsmooth functions that are convex and differentiable almost ... See full document
56
Trainable Undersampling for Class-Imbalance Learning
... evaluation metric. We solve the non-differentiable optimization problem for training the data sampler via reinforcement ...evaluation metric optimization into the data sampling process, the ... See full document
8
A Direct Method for Building Sparse Kernel Learning Algorithms
... kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive ... See full document
22
Multi-Stage Multi-Task Feature Learning
... The formulation in Equation (1) is non-convex and is difficult to solve. In this paper, we propose an algorithm called Multi-Stage Multi-Task Feature Learning (MSMTFL) to solve the optimization ... See full document
32
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
... online convex optimization problems, the regret bound can be improved to O(lnt) (Hazan et ...deterministic optimization with strong convexity assumptions; see, for example, Nesterov (2004, Chapter 2) ... See full document
54
SOME FIXED POINT RESULTS IN THE GENERALIZED CONVEX METRIC SPACES
... Faik GRSOYserves as associate professor for the Department of Mathematics at the Adiyaman University, Adiyaman, Turkey. He received a Ph.D. degree in mathe- matics at the Yildiz Technical University under the ... See full document
13
Distance Metric Learning with Eigenvalue Optimization
... metric learning. Within this context, we first proposed a novel metric learning model which was shown to be equivalent to a well-known eigenvalue optimization problem (Overton, 1988; ... See full document
26
On Near Optimality of One-sample Update for Joint Detection and Estimation
... 1 Although both fields, sequential analysis and online convex optimization, study sequential data, the precise connection between them is not clear, partly because the performance metric[r] ... See full document
22
Mask-Constrained Power Synthesis of Large and Arbitrary Arraysas a Few-Samples Global Optimization
... nested optimization where the external global optimization acts on the field’s phase shifts over a minimal number of ‘control points’ located into the target region whereas the internal optimization ... See full document
13
Filter Design Problems with Convex Optimization
... of convex optimization has been studied for about one ...of convex optimization have been discovered in various fields of applied science and engineering, such as automatic control system, ... See full document
6
A convergence result on random products of mappings in metric trees
... As the authors did in [14], one inspires itself from the Amemiya and Ando ’ s work in Hilbert spaces to extend it to other underlying spaces. In particular the authors in [14] introduced the concepts of (W) and (S) ... See full document
10
Fixed Points of Single- and Set-Valued Mappings in Uniformly Convex Metric Spaces with No Metric Convexity
... closed convex hull of the sequence, which easily follows from the very well-known fact that the metric projection onto closed convex subsets of a complete CAT0 space is nonexpansive see 2 for ... See full document
16
Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization
... Despite its practical importance, there does not seem to be many systematic approaches to the problem of computing bifurcation margins. In reference [30], Dobson proposed two methods for computing locally closest ... See full document
135
Global passivity enforcement via convex optimization
... that optimization can be used to enforce the passivity of rational functions employed to approximate a power network (or components) ...a convex optimization problem, to all the frequency points in ... See full document
13
Fixed Points for Multivalued Mappings in Uniformly Convex Metric Spaces
... a metric space X is said to be uniquely proximinal if each point x ∈ X has a unique nearest point in ...A convex metric space X is said to have property (C) if every decreasing sequence of nonempty ... See full document
9
Convex optimization using quantum oracles
... continuous optimization paradigms is convex optimization, which optimizes a convex function over a convex set that is given explicitly (by a set of constraints) or implicitly (by an ... See full document
29
A Different Approach to Cone Convex Optimization
... We have shown that, with Slater-type cone constraint quailfication, convexity of the feasible set can replace the cone-convexity or any of its generalization of the constraint functions,[r] ... See full document
6
Sparse Recovery via Convex Optimization
... The main contribution of this chapter is to show that it is possible to recover a signal from measurements when it is known that the signal is sparse in a given dictionary, and where the[r] ... See full document
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