29,006 research outputs found
Recommended from our members
Learning music similarity from relative user ratings
Computational modelling of music similarity is an increasingly important part of personalisation and optimisation in music information retrieval and research in music perception and cognition. The use of relative similarity ratings is a new and promising approach to modelling similarity that avoids well known problems with absolute ratings. In this article, we use relative ratings from the MagnaTagATune dataset with new and existing variants of state-of-the-art algorithms and provide the first comprehensive and rigorous evaluation of this approach. We compare metric learning based on support vector machines (SVMs) and metric-learning-to-rank (MLR), including a diagonal and a novel weighted variant, and relative distance learning with neural networks (RDNN). We further evaluate the effectiveness of different high and low level audio features and genre data, as well as dimensionality reduction methods, weighting of similarity ratings, and different sampling methods. Our results show that music similarity measures learnt on relative ratings can be significantly better than a standard Euclidian metric, depending on the choice of learning algorithm, feature sets and application scenario. MLR and SVM outperform DMLR and RDNN, while MLR with weighted ratings leads to no further performance gain. Timbral and music-structural features are most effective, and all features jointly are significantly better than any other combination of feature sets. Sharing audio clips (but not the similarity ratings) between test and training sets improves performance, in particular for the SVM-based methods, which is useful for some applications scenarios. A testing framework has been implemented in Matlab and made publicly available http://mi.soi.city.ac.uk/datasets/ir2012framework so that these results are reproducible
Two-Stage Metric Learning
In this paper, we present a novel two-stage metric learning algorithm. We
first map each learning instance to a probability distribution by computing its
similarities to a set of fixed anchor points. Then, we define the distance in
the input data space as the Fisher information distance on the associated
statistical manifold. This induces in the input data space a new family of
distance metric with unique properties. Unlike kernelized metric learning, we
do not require the similarity measure to be positive semi-definite. Moreover,
it can also be interpreted as a local metric learning algorithm with well
defined distance approximation. We evaluate its performance on a number of
datasets. It outperforms significantly other metric learning methods and SVM.Comment: Accepted for publication in ICML 201
Similarity Learning for High-Dimensional Sparse Data
A good measure of similarity between data points is crucial to many tasks in
machine learning. Similarity and metric learning methods learn such measures
automatically from data, but they do not scale well respect to the
dimensionality of the data. In this paper, we propose a method that can learn
efficiently similarity measure from high-dimensional sparse data. The core idea
is to parameterize the similarity measure as a convex combination of rank-one
matrices with specific sparsity structures. The parameters are then optimized
with an approximate Frank-Wolfe procedure to maximally satisfy relative
similarity constraints on the training data. Our algorithm greedily
incorporates one pair of features at a time into the similarity measure,
providing an efficient way to control the number of active features and thus
reduce overfitting. It enjoys very appealing convergence guarantees and its
time and memory complexity depends on the sparsity of the data instead of the
dimension of the feature space. Our experiments on real-world high-dimensional
datasets demonstrate its potential for classification, dimensionality reduction
and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS 2015). Matlab code:
https://github.com/bellet/HDS
Template Adaptation for Face Verification and Identification
Face recognition performance evaluation has traditionally focused on
one-to-one verification, popularized by the Labeled Faces in the Wild dataset
for imagery and the YouTubeFaces dataset for videos. In contrast, the newly
released IJB-A face recognition dataset unifies evaluation of one-to-many face
identification with one-to-one face verification over templates, or sets of
imagery and videos for a subject. In this paper, we study the problem of
template adaptation, a form of transfer learning to the set of media in a
template. Extensive performance evaluations on IJB-A show a surprising result,
that perhaps the simplest method of template adaptation, combining deep
convolutional network features with template specific linear SVMs, outperforms
the state-of-the-art by a wide margin. We study the effects of template size,
negative set construction and classifier fusion on performance, then compare
template adaptation to convolutional networks with metric learning, 2D and 3D
alignment. Our unexpected conclusion is that these other methods, when combined
with template adaptation, all achieve nearly the same top performance on IJB-A
for template-based face verification and identification
- …