A survey on metric learning for feature vectors and structured data
The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
An effective image representation method using kernel classification
H Wang, J Wang - 2014 IEEE 26th international conference on …, 2014 - ieeexplore.ieee.org
The learning of image representation is always the most important problem in computer
vision community. In this paper, we propose a novel image representation method by …
vision community. In this paper, we propose a novel image representation method by …
CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction
Motivation: Protein homology detection, a fundamental problem in computational biology, is
an indispensable step toward predicting protein structures and understanding protein …
an indispensable step toward predicting protein structures and understanding protein …
LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone
Background Protein-ligand binding is important for some proteins to perform their functions.
Protein-ligand binding sites are the residues of proteins that physically bind to ligands …
Protein-ligand binding sites are the residues of proteins that physically bind to ligands …
Aprendizaje supervisado de funciones de distancia: estado del arte
B Nguyen Cong, JL Rivero Pérez… - Revista Cubana de …, 2015 - scielo.sld.cu
La selección de una función de distancia adecuada es fundamental para los algoritmos de
aprendizaje basados en instancias. Tal función de distancia dicta el éxito o el fracaso de …
aprendizaje basados en instancias. Tal función de distancia dicta el éxito o el fracaso de …
Multi-granularity distance metric learning via neighborhood granule margin maximization
Learning a distance metric from training samples is often a crucial step in machine learning
and pattern recognition. Locality, compactness and consistency are considered as the key …
and pattern recognition. Locality, compactness and consistency are considered as the key …
Robust and effective metric learning using capped trace norm: Metric learning via capped trace norm
Metric learning aims at automatically learning a metric from pair or triplet based constraints
in data, and it can be potentially beneficial whenever the notion of metric between instances …
in data, and it can be potentially beneficial whenever the notion of metric between instances …
Supervised logeuclidean metric learning for symmetric positive definite matrices
F Yger, M Sugiyama - arXiv preprint arXiv:1502.03505, 2015 - arxiv.org
Metric learning has been shown to be highly effective to improve the performance of nearest
neighbor classification. In this paper, we address the problem of metric learning for …
neighbor classification. In this paper, we address the problem of metric learning for …
Maximum mutual information regularized classification
In this paper, a novel pattern classification approach is proposed by regularizing the
classifier learning to maximize mutual information between the classification response and …
classifier learning to maximize mutual information between the classification response and …
Similarity metric learning on perturbational datasets improves functional identification of perturbations
Abstract Analysis of high-throughput perturbational datasets, including the Next Generation
Connectivity Map (L1000) and the Cell Painting projects, uses similarity metrics to identify …
Connectivity Map (L1000) and the Cell Painting projects, uses similarity metrics to identify …