49,024 research outputs found
Ultra-Scalable Spectral Clustering and Ensemble Clustering
This paper focuses on scalability and robustness of spectral clustering for
extremely large-scale datasets with limited resources. Two novel algorithms are
proposed, namely, ultra-scalable spectral clustering (U-SPEC) and
ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative
selection strategy and a fast approximation method for K-nearest
representatives are proposed for the construction of a sparse affinity
sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the
transfer cut is then utilized to efficiently partition the graph and obtain the
clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated
into an ensemble clustering framework to enhance the robustness of U-SPEC while
maintaining high efficiency. Based on the ensemble generation via multiple
U-SEPC's, a new bipartite graph is constructed between objects and base
clusters and then efficiently partitioned to achieve the consensus clustering
result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time
and space complexity, and are capable of robustly and efficiently partitioning
ten-million-level nonlinearly-separable datasets on a PC with 64GB memory.
Experiments on various large-scale datasets have demonstrated the scalability
and robustness of our algorithms. The MATLAB code and experimental data are
available at https://www.researchgate.net/publication/330760669.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering,
201
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is the first study which demonstrates how the architectures
for learning word embeddings can be applied to this challenging
syntactic-semantic task. Our method uses cross-lingual translation pairs to tie
each of the six target languages into a bilingual vector space with English,
jointly specialising the representations to encode the relational information
from English VerbNet. A standard clustering algorithm is then run on top of the
VerbNet-specialised representations, using vector dimensions as features for
learning verb classes. Our results show that the proposed cross-lingual
transfer approach sets new state-of-the-art verb classification performance
across all six target languages explored in this work.Comment: EMNLP 2017 (long paper
Multiscale photosynthetic exciton transfer
Photosynthetic light harvesting provides a natural blueprint for
bioengineered and biomimetic solar energy and light detection technologies.
Recent evidence suggests some individual light harvesting protein complexes
(LHCs) and LHC subunits efficiently transfer excitons towards chemical reaction
centers (RCs) via an interplay between excitonic quantum coherence, resonant
protein vibrations, and thermal decoherence. The role of coherence in vivo is
unclear however, where excitons are transferred through multi-LHC/RC aggregates
over distances typically large compared with intra-LHC scales. Here we assess
the possibility of long-range coherent transfer in a simple chromophore network
with disordered site and transfer coupling energies. Through renormalization we
find that, surprisingly, decoherence is diminished at larger scales, and
long-range coherence is facilitated by chromophoric clustering. Conversely,
static disorder in the site energies grows with length scale, forcing
localization. Our results suggest sustained coherent exciton transfer may be
possible over distances large compared with nearest-neighbour (n-n) chromophore
separations, at physiological temperatures, in a clustered network with small
static disorder. This may support findings suggesting long-range coherence in
algal chloroplasts, and provides a framework for engineering large chromophore
or quantum dot high-temperature exciton transfer networks.Comment: 9 pages, 6 figures. A significantly updated version is now published
online by Nature Physics (2012
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
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