Image retrieval: Ideas, influences, and trends of the new age
We have witnessed great interest and a wealth of promise in content-based image retrieval
as an emerging technology. While the last decade laid foundation to such promise, it also …
as an emerging technology. While the last decade laid foundation to such promise, it also …
A survey on indexing techniques for big data: taxonomy and performance evaluation
The explosive growth in volume, velocity, and diversity of data produced by mobile devices
and cloud applications has contributed to the abundance of data or 'big data.'Available …
and cloud applications has contributed to the abundance of data or 'big data.'Available …
Heterogeneous network representation learning: A unified framework with survey and benchmark
Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …
heterogeneous networks have been widely used as a more powerful, realistic, and generic …
Fast random walk with restart and its applications
How closely related are two nodes in a graph? How to compute this score quickly, on huge,
disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance …
disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance …
Oops, my tests broke the build: An explorative analysis of travis ci with github
Continuous Integration (CI) has become a best practice of modern software development.
Yet, at present, we have a shortfall of insight into the testing practices that are common in CI …
Yet, at present, we have a shortfall of insight into the testing practices that are common in CI …
[PDF][PDF] Distance metric learning: A comprehensive survey
L Yang, R Jin - Michigan State Universiy, 2006 - cse.msu.edu
Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the
distance metric for the input data patterns. Distance Metric learning is to learn a distance …
distance metric for the input data patterns. Distance Metric learning is to learn a distance …
Learning a Mahalanobis distance metric for data clustering and classification
Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …
general problem of learning from pairwise constraints in the form of must-links and cannot …
Multimodal graph-based reranking for web image search
This paper introduces a web image search reranking approach that explores multiple
modalities in a graph-based learning scheme. Different from the conventional methods that …
modalities in a graph-based learning scheme. Different from the conventional methods that …
A multimedia retrieval framework based on semi-supervised ranking and relevance feedback
We present a new framework for multimedia content analysis and retrieval which consists of
two independent algorithms. First, we propose a new semi-supervised algorithm called …
two independent algorithms. First, we propose a new semi-supervised algorithm called …
Survey on distance metric learning and dimensionality reduction in data mining
Distance metric learning is a fundamental problem in data mining and knowledge discovery.
Many representative data mining algorithms, such as k k-nearest neighbor classifier …
Many representative data mining algorithms, such as k k-nearest neighbor classifier …