Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metr…
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8 results for “LMNN”
This study observes accuracy variations of KNN, SVM, LMNN, and ENN on 11 UCI datasets.
problem To observe the variation of accuracies of KNN, SVM, LMNN, and ENN algorithms on different datasets.
method Implemented KNN, SVM, LMNN, and ENN on 11 UCI datasets and analyzed their accuracy.
result Comparison of algorithm performances on each dataset.
A new method for learning distance metrics for K-NN classification.
problem Improving the performance of K-NN classifier by learning an appropriate distance metric.
method Designing a continuous decision function for K-NN and minimizing its continuous empirical risk function.
result The proposed ANN algorithm outperforms existing methods like LMNN, NCA, and pairwise constraints.
We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive properties: (i) an innate geometric app…
We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data. While metric learning enables a problem-adapted representation of data, the majority of methods has been proposed for vectorial data onl…
We develop a robust metric learning method for noisy data.
problem Learning Mahalanobis metrics in the presence of adversarial label noise.
method Formulated as an optimization problem, solved using FPTAS with nearly-linear running time.
result Obtained a fully-parallelizable algorithm that recovers a nearly-optimal metric space.
New RF dissimilarity measures improve multi-view learning accuracy.
problem Improving multi-view learning accuracy in HDLSS problems.
method Modified Random Forest proximity measures for HDLSS multi-view classification.
result Second method significantly more accurate than other state-of-the-art methods.
New metric learning approach for tree data reduces computation cost.
problem Efficiently computing distances between ordered labeled trees.
method Introduced pq-grams and a differentiable weighted pq-gram distance, combined with LMNN for optimization.
result Significantly reduces computation time for tree classification problems.