1,255 research outputs found
近傍法における距離・類似度尺度のデータ中心化 -ハブネスの軽減-
Open House, ISM in Tachikawa, 2015.6.19統計数理研究所オープンハウス(立川)、H27.6.19ポスター発
Ridge Regression, Hubness, and Zero-Shot Learning
This paper discusses the effect of hubness in zero-shot learning, when ridge
regression is used to find a mapping between the example space to the label
space. Contrary to the existing approach, which attempts to find a mapping from
the example space to the label space, we show that mapping labels into the
example space is desirable to suppress the emergence of hubs in the subsequent
nearest neighbor search step. Assuming a simple data model, we prove that the
proposed approach indeed reduces hubness. This was verified empirically on the
tasks of bilingual lexicon extraction and image labeling: hubness was reduced
with both of these tasks and the accuracy was improved accordingly.Comment: To be presented at ECML/PKDD 201
Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (Extended Version)
Although the ``scale-free'' literature is large and growing, it gives neither
a precise definition of scale-free graphs nor rigorous proofs of many of their
claimed properties. In fact, it is easily shown that the existing theory has
many inherent contradictions and verifiably false claims. In this paper, we
propose a new, mathematically precise, and structural definition of the extent
to which a graph is scale-free, and prove a series of results that recover many
of the claimed properties while suggesting the potential for a rich and
interesting theory. With this definition, scale-free (or its opposite,
scale-rich) is closely related to other structural graph properties such as
various notions of self-similarity (or respectively, self-dissimilarity).
Scale-free graphs are also shown to be the likely outcome of random
construction processes, consistent with the heuristic definitions implicit in
existing random graph approaches. Our approach clarifies much of the confusion
surrounding the sensational qualitative claims in the scale-free literature,
and offers rigorous and quantitative alternatives.Comment: 44 pages, 16 figures. The primary version is to appear in Internet
Mathematics (2005
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
In this work, we present a post-processing solution to address the hubness
problem in cross-modal retrieval, a phenomenon where a small number of gallery
data points are frequently retrieved, resulting in a decline in retrieval
performance. We first theoretically demonstrate the necessity of incorporating
both the gallery and query data for addressing hubness as hubs always exhibit
high similarity with gallery and query data. Second, building on our
theoretical results, we propose a novel framework, Dual Bank Normalization
(DBNorm). While previous work has attempted to alleviate hubness by only
utilizing the query samples, DBNorm leverages two banks constructed from the
query and gallery samples to reduce the occurrence of hubs during inference.
Next, to complement DBNorm, we introduce two novel methods, dual inverted
softmax and dual dynamic inverted softmax, for normalizing similarity based on
the two banks. Specifically, our proposed methods reduce the similarity between
hubs and queries while improving the similarity between non-hubs and queries.
Finally, we present extensive experimental results on diverse language-grounded
benchmarks, including text-image, text-video, and text-audio, demonstrating the
superior performance of our approaches compared to previous methods in
addressing hubness and boosting retrieval performance. Our code is available at
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.Comment: Accepted by EMNLP 202
On the Selection of Anchors and Targets for Video Hyperlinking
A problem not well understood in video hyperlinking is what qualifies a
fragment as an anchor or target. Ideally, anchors provide good starting points
for navigation, and targets supplement anchors with additional details while
not distracting users with irrelevant, false and redundant information. The
problem is not trivial for intertwining relationship between data
characteristics and user expectation. Imagine that in a large dataset, there
are clusters of fragments spreading over the feature space. The nature of each
cluster can be described by its size (implying popularity) and structure
(implying complexity). A principle way of hyperlinking can be carried out by
picking centers of clusters as anchors and from there reach out to targets
within or outside of clusters with consideration of neighborhood complexity.
The question is which fragments should be selected either as anchors or
targets, in one way to reflect the rich content of a dataset, and meanwhile to
minimize the risk of frustrating user experience. This paper provides some
insights to this question from the perspective of hubness and local intrinsic
dimensionality, which are two statistical properties in assessing the
popularity and complexity of data space. Based these properties, two novel
algorithms are proposed for low-risk automatic selection of anchors and
targets.Comment: ACM International Conference on Multimedia Retrieval (ICMR), 2017.
(Oral
Nonparametric Bayes Modeling of Populations of Networks
Replicated network data are increasingly available in many research fields.
In connectomic applications, inter-connections among brain regions are
collected for each patient under study, motivating statistical models which can
flexibly characterize the probabilistic generative mechanism underlying these
network-valued data. Available models for a single network are not designed
specifically for inference on the entire probability mass function of a
network-valued random variable and therefore lack flexibility in characterizing
the distribution of relevant topological structures. We propose a flexible
Bayesian nonparametric approach for modeling the population distribution of
network-valued data. The joint distribution of the edges is defined via a
mixture model which reduces dimensionality and efficiently incorporates network
information within each mixture component by leveraging latent space
representations. The formulation leads to an efficient Gibbs sampler and
provides simple and coherent strategies for inference and goodness-of-fit
assessments. We provide theoretical results on the flexibility of our model and
illustrate improved performance --- compared to state-of-the-art models --- in
simulations and application to human brain networks
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