26,299 research outputs found
Pattern vectors from algebraic graph theory
Graphstructures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner. We extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a Hermitian property matrix that can be viewed as a complex analogue of the Laplacian. To embed the graphs in a pattern space, we explore whether the vectors of invariants can be embedded in a low- dimensional space using a number of alternative strategies, including principal components analysis ( PCA), multidimensional scaling ( MDS), and locality preserving projection ( LPP). Experimentally, we demonstrate that the embeddings result in well- defined graph clusters. Our experiments with the spectral representation involve both synthetic and real- world data. The experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure. The real- world experiments show that the method can be used to locate clusters of graphs
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
High resolution satellite image sequences are multidimensional signals
composed of spatio-temporal patterns associated to numerous and various
phenomena. Bayesian methods have been previously proposed in (Heas and Datcu,
2005) to code the information contained in satellite image sequences in a graph
representation using Bayesian methods. Based on such a representation, this
paper further presents a supervised learning methodology of semantics
associated to spatio-temporal patterns occurring in satellite image sequences.
It enables the recognition and the probabilistic retrieval of similar events.
Indeed, graphs are attached to statistical models for spatio-temporal
processes, which at their turn describe physical changes in the observed scene.
Therefore, we adjust a parametric model evaluating similarity types between
graph patterns in order to represent user-specific semantics attached to
spatio-temporal phenomena. The learning step is performed by the incremental
definition of similarity types via user-provided spatio-temporal pattern
examples attached to positive or/and negative semantics. From these examples,
probabilities are inferred using a Bayesian network and a Dirichlet model. This
enables to links user interest to a specific similarity model between graph
patterns. According to the current state of learning, semantic posterior
probabilities are updated for all possible graph patterns so that similar
spatio-temporal phenomena can be recognized and retrieved from the image
sequence. Few experiments performed on a multi-spectral SPOT image sequence
illustrate the proposed spatio-temporal recognition method
Pattern tree-based XOLAP rollup operator for XML complex hierarchies
With the rise of XML as a standard for representing business data, XML data
warehousing appears as a suitable solution for decision-support applications.
In this context, it is necessary to allow OLAP analyses on XML data cubes.
Thus, XQuery extensions are needed. To define a formal framework and allow
much-needed performance optimizations on analytical queries expressed in
XQuery, defining an algebra is desirable. However, XML-OLAP (XOLAP) algebras
from the literature still largely rely on the relational model. Hence, we
propose in this paper a rollup operator based on a pattern tree in order to
handle multidimensional XML data expressed within complex hierarchies
Efficient Retrieval of Similar Time Sequences Using DFT
We propose an improvement of the known DFT-based indexing technique for fast
retrieval of similar time sequences. We use the last few Fourier coefficients
in the distance computation without storing them in the index since every
coefficient at the end is the complex conjugate of a coefficient at the
beginning and as strong as its counterpart. We show analytically that this
observation can accelerate the search time of the index by more than a factor
of two. This result was confirmed by our experiments, which were carried out on
real stock prices and synthetic data
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
Similarity matching and join of time series data streams has gained a lot of
relevance in today's world that has large streaming data. This process finds
wide scale application in the areas of location tracking, sensor networks,
object positioning and monitoring to name a few. However, as the size of the
data stream increases, the cost involved to retain all the data in order to aid
the process of similarity matching also increases. We develop a novel framework
to addresses the following objectives. Firstly, Dimension reduction is
performed in the preprocessing stage, where large stream data is segmented and
reduced into a compact representation such that it retains all the crucial
information by a technique called Multi-level Segment Means (MSM). This reduces
the space complexity associated with the storage of large time-series data
streams. Secondly, it incorporates effective Similarity Matching technique to
analyze if the new data objects are symmetric to the existing data stream. And
finally, the Pruning Technique that filters out the pseudo data object pairs
and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction
Factor. We have performed exhaustive experimental trials to show that the
proposed framework is both efficient and competent in comparison with earlier
works.Comment: 20 pages,8 figures, 6 Table
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