32,040 research outputs found
Neural activity classification with machine learning models trained on interspike interval series data
The flow of information through the brain is reflected by the activity
patterns of neural cells. Indeed, these firing patterns are widely used as
input data to predictive models that relate stimuli and animal behavior to the
activity of a population of neurons. However, relatively little attention was
paid to single neuron spike trains as predictors of cell or network properties
in the brain. In this work, we introduce an approach to neuronal spike train
data mining which enables effective classification and clustering of neuron
types and network activity states based on single-cell spiking patterns. This
approach is centered around applying state-of-the-art time series
classification/clustering methods to sequences of interspike intervals recorded
from single neurons. We demonstrate good performance of these methods in tasks
involving classification of neuron type (e.g. excitatory vs. inhibitory cells)
and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep
states) on an open-access cortical spiking activity dataset
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 good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Tree-Independent Dual-Tree Algorithms
Dual-tree algorithms are a widely used class of branch-and-bound algorithms.
Unfortunately, developing dual-tree algorithms for use with different trees and
problems is often complex and burdensome. We introduce a four-part logical
split: the tree, the traversal, the point-to-point base case, and the pruning
rule. We provide a meta-algorithm which allows development of dual-tree
algorithms in a tree-independent manner and easy extension to entirely new
types of trees. Representations are provided for five common algorithms; for
k-nearest neighbor search, this leads to a novel, tighter pruning bound. The
meta-algorithm also allows straightforward extensions to massively parallel
settings.Comment: accepted in ICML 201
Ranked List Loss for Deep Metric Learning
The objective of deep metric learning (DML) is to learn embeddings that can
capture semantic similarity and dissimilarity information among data points.
Existing pairwise or tripletwise loss functions used in DML are known to suffer
from slow convergence due to a large proportion of trivial pairs or triplets as
the model improves. To improve this, ranking-motivated structured losses are
proposed recently to incorporate multiple examples and exploit the structured
information among them. They converge faster and achieve state-of-the-art
performance. In this work, we unveil two limitations of existing
ranking-motivated structured losses and propose a novel ranked list loss to
solve both of them. First, given a query, only a fraction of data points is
incorporated to build the similarity structure. Consequently, some useful
examples are ignored and the structure is less informative. To address this, we
propose to build a set-based similarity structure by exploiting all instances
in the gallery. The learning setting can be interpreted as few-shot retrieval:
given a mini-batch, every example is iteratively used as a query, and the rest
ones compose the gallery to search, i.e., the support set in few-shot setting.
The rest examples are split into a positive set and a negative set. For every
mini-batch, the learning objective of ranked list loss is to make the query
closer to the positive set than to the negative set by a margin. Second,
previous methods aim to pull positive pairs as close as possible in the
embedding space. As a result, the intraclass data distribution tends to be
extremely compressed. In contrast, we propose to learn a hypersphere for each
class in order to preserve useful similarity structure inside it, which
functions as regularisation. Extensive experiments demonstrate the superiority
of our proposal by comparing with the state-of-the-art methods.Comment: Accepted to T-PAMI. Therefore, to read the offical version, please go
to IEEE Xplore. Fine-grained image retrieval task. Our source code is
available online: https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DM
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
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