1,716 research outputs found
Evolutionary Self-Expressive Models for Subspace Clustering
The problem of organizing data that evolves over time into clusters is
encountered in a number of practical settings. We introduce evolutionary
subspace clustering, a method whose objective is to cluster a collection of
evolving data points that lie on a union of low-dimensional evolving subspaces.
To learn the parsimonious representation of the data points at each time step,
we propose a non-convex optimization framework that exploits the
self-expressiveness property of the evolving data while taking into account
representation from the preceding time step. To find an approximate solution to
the aforementioned non-convex optimization problem, we develop a scheme based
on alternating minimization that both learns the parsimonious representation as
well as adaptively tunes and infers a smoothing parameter reflective of the
rate of data evolution. The latter addresses a fundamental challenge in
evolutionary clustering -- determining if and to what extent one should
consider previous clustering solutions when analyzing an evolving data
collection. Our experiments on both synthetic and real-world datasets
demonstrate that the proposed framework outperforms state-of-the-art static
subspace clustering algorithms and existing evolutionary clustering schemes in
terms of both accuracy and running time, in a range of scenarios
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
This paper introduces an automated skill acquisition framework in
reinforcement learning which involves identifying a hierarchical description of
the given task in terms of abstract states and extended actions between
abstract states. Identifying such structures present in the task provides ways
to simplify and speed up reinforcement learning algorithms. These structures
also help to generalize such algorithms over multiple tasks without relearning
policies from scratch. We use ideas from dynamical systems to find metastable
regions in the state space and associate them with abstract states. The
spectral clustering algorithm PCCA+ is used to identify suitable abstractions
aligned to the underlying structure. Skills are defined in terms of the
sequence of actions that lead to transitions between such abstract states. The
connectivity information from PCCA+ is used to generate these skills or
options. These skills are independent of the learning task and can be
efficiently reused across a variety of tasks defined over the same model. This
approach works well even without the exact model of the environment by using
sample trajectories to construct an approximate estimate. We also present our
approach to scaling the skill acquisition framework to complex tasks with large
state spaces for which we perform state aggregation using the representation
learned from an action conditional video prediction network and use the skill
acquisition framework on the aggregated state space.Comment: Revised version of ICML 16 Abstraction in Reinforcement Learning
workshop pape
Efficient Unsupervised Temporal Segmentation of Motion Data
We introduce a method for automated temporal segmentation of human motion
data into distinct actions and compositing motion primitives based on
self-similar structures in the motion sequence. We use neighbourhood graphs for
the partitioning and the similarity information in the graph is further
exploited to cluster the motion primitives into larger entities of semantic
significance. The method requires no assumptions about the motion sequences at
hand and no user interaction is required for the segmentation or clustering. In
addition, we introduce a feature bundling preprocessing technique to make the
segmentation more robust to noise, as well as a notion of motion symmetry for
more refined primitive detection. We test our method on several sensor
modalities, including markered and markerless motion capture as well as on
electromyograph and accelerometer recordings. The results highlight our
system's capabilities for both segmentation and for analysis of the finer
structures of motion data, all in a completely unsupervised manner.Comment: 15 pages, submitted to TPAM
Fast Randomized Singular Value Thresholding for Low-rank Optimization
Rank minimization can be converted into tractable surrogate problems, such as
Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related
to NNM, or WNNM, can be solved iteratively by applying a closed-form proximal
operator, called Singular Value Thresholding (SVT), or Weighted SVT, but they
suffer from high computational cost of Singular Value Decomposition (SVD) at
each iteration. We propose a fast and accurate approximation method for SVT,
that we call fast randomized SVT (FRSVT), with which we avoid direct
computation of SVD. The key idea is to extract an approximate basis for the
range of the matrix from its compressed matrix. Given the basis, we compute
partial singular values of the original matrix from the small factored matrix.
In addition, by developping a range propagation method, our method further
speeds up the extraction of approximate basis at each iteration. Our
theoretical analysis shows the relationship between the approximation bound of
SVD and its effect to NNM via SVT. Along with the analysis, our empirical
results quantitatively and qualitatively show that our approximation rarely
harms the convergence of the host algorithms. We assess the efficiency and
accuracy of the proposed method on various computer vision problems, e.g.,
subspace clustering, weather artifact removal, and simultaneous multi-image
alignment and rectification.Comment: Appeared in CVPR 2015, and under major revision of TPAMI. Source code
is available on http://thoh.kaist.ac.k
Temporal Human Action Segmentation via Dynamic Clustering
We present an effective dynamic clustering algorithm for the task of temporal
human action segmentation, which has comprehensive applications such as
robotics, motion analysis, and patient monitoring. Our proposed algorithm is
unsupervised, fast, generic to process various types of features, and
applicable in both the online and offline settings. We perform extensive
experiments of processing data streams, and show that our algorithm achieves
the state-of-the-art results for both online and offline settings.Comment: comparing with the 1st version, only corrected typo
Visualizing Time-Varying Particle Flows with Diffusion Geometry
The tasks of identifying separation structures and clusters in flow data are
fundamental to flow visualization. Significant work has been devoted to these
tasks in flow represented by vector fields, but there are unique challenges in
addressing these tasks for time-varying particle data. The unstructured nature
of particle data, nonuniform and sparse sampling, and the inability to access
arbitrary particles in space-time make it difficult to define separation and
clustering for particle data. We observe that weaker notions of separation and
clustering through continuous measures of these structures are meaningful when
coupled with user exploration. We achieve this goal by defining a measure of
particle similarity between pairs of particles. More specifically, separation
occurs when spatially-localized particles are dissimilar, while clustering is
characterized by sets of particles that are similar to one another. To be
robust to imperfections in sampling we use diffusion geometry to compute
particle similarity. Diffusion geometry is parameterized by a scale that allows
a user to explore separation and clustering in a continuous manner. We
illustrate the benefits of our technique on a variety of 2D and 3D flow
datasets, from particles integrated in fluid simulations based on time-varying
vector fields, to particle-based simulations in astrophysics.Comment: 14 pages, 16 figures, under revie
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
On the organization of grid and place cells: Neural de-noising via subspace learning
Place cells in the hippocampus are active when an animal visits a certain
location (referred to as a place field) within an environment. Grid cells in
the medial entorhinal cortex (MEC) respond at multiple locations, with firing
fields that form a periodic and hexagonal tiling of the environment. The joint
activity of grid and place cell populations, as a function of location, forms a
neural code for space. An ensemble of codes is generated by varying grid and
place cell population parameters. For each code in this ensemble, codewords are
generated by stimulating a network with a discrete set of locations. In this
manuscript, we develop an understanding of the relationships between coding
theoretic properties of these combined populations and code construction
parameters. These relationships are revisited by measuring the performances of
biologically realizable algorithms implemented by networks of place and grid
cell populations, as well as constraint neurons, which perform de-noising
operations. Objectives of this work include the investigation of coding
theoretic limitations of the mammalian neural code for location and how
communication between grid and place cell networks may improve the accuracy of
each population's representation. Simulations demonstrate that de-noising
mechanisms analyzed here can significantly improve fidelity of this neural
representation of space. Further, patterns observed in connectivity of each
population of simulated cells suggest that
inter-hippocampal-medial-entorhinal-cortical connectivity decreases downward
along the dorsoventral axis
Automatic Face Recognition from Video
The objective of this work is to automatically recognize faces from video
sequences in a realistic, unconstrained setup in which illumination conditions
are extreme and greatly changing, viewpoint and user motion pattern have a wide
variability, and video input is of low quality. At the centre of focus are face
appearance manifolds: this thesis presents a significant advance of their
understanding and application in the sphere of face recognition. The two main
contributions are the Generic Shape-Illumination Manifold recognition algorithm
and the Anisotropic Manifold Space clustering. The Generic Shape-Illumination
Manifold is evaluated on a large data corpus acquired in real-world conditions
and its performance is shown to greatly exceed that of state-of-the-art methods
in the literature and the best performing commercial software. Empirical
evaluation of the Anisotropic Manifold Space clustering on a popular situation
comedy is also described with excellent preliminary results.Comment: Doctor of Philosophy (PhD) dissertation, University of Cambridge,
200
Low-Rank Modeling and Its Applications in Image Analysis
Low-rank modeling generally refers to a class of methods that solve problems
by representing variables of interest as low-rank matrices. It has achieved
great success in various fields including computer vision, data mining, signal
processing and bioinformatics. Recently, much progress has been made in
theories, algorithms and applications of low-rank modeling, such as exact
low-rank matrix recovery via convex programming and matrix completion applied
to collaborative filtering. These advances have brought more and more
attentions to this topic. In this paper, we review the recent advance of
low-rank modeling, the state-of-the-art algorithms, and related applications in
image analysis. We first give an overview to the concept of low-rank modeling
and challenging problems in this area. Then, we summarize the models and
algorithms for low-rank matrix recovery and illustrate their advantages and
limitations with numerical experiments. Next, we introduce a few applications
of low-rank modeling in the context of image analysis. Finally, we conclude
this paper with some discussions.Comment: To appear in ACM Computing Survey
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