11,181 research outputs found
Change detection in categorical evolving data streams
Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution.
To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream
A High-Fidelity Realization of the Euclid Code Comparison -body Simulation with Abacus
We present a high-fidelity realization of the cosmological -body
simulation from the Schneider et al. (2016) code comparison project. The
simulation was performed with our Abacus -body code, which offers high force
accuracy, high performance, and minimal particle integration errors. The
simulation consists of particles in a box,
for a particle mass of with $10\
h^{-1}\mathrm{kpc}z=0<0.3\%k<10\
\mathrm{Mpc}^{-1}h0.01\%$. Simulation snapshots are available at
http://nbody.rc.fas.harvard.edu/public/S2016 .Comment: 13 pages, 8 figures. Minor changes to match MNRAS accepted versio
SOM-based algorithms for qualitative variables
It is well known that the SOM algorithm achieves a clustering of data which
can be interpreted as an extension of Principal Component Analysis, because of
its topology-preserving property. But the SOM algorithm can only process
real-valued data. In previous papers, we have proposed several methods based on
the SOM algorithm to analyze categorical data, which is the case in survey
data. In this paper, we present these methods in a unified manner. The first
one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the
modalities, while the two others (Kohonen Multiple Correspondence Analysis with
individuals, KMCA\_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take
into account the individuals, and the modalities simultaneously.Comment: Special Issue apr\`{e}s WSOM 03 \`{a} Kitakiush
Improving the family orientation process in Cuban Special Schools trough Nearest Prototype classification
Cuban Schools for children with Affective â Behavioral Maladies (SABM) have as goal to accomplish a major change in children behavior, to insert them effectively into society. One of the key elements in this objective is to give an adequate orientation to the childrenâs families; due to the family is one of the most important educational contexts in which the children will develop their personality. The family orientation process in SABM involves clustering and classification of mixed type data with non-symmetric similarity functions. To improve this process, this paper includes some novel characteristics in clustering and prototype selection. The proposed approach uses a hierarchical clustering based on compact sets, making it suitable for dealing with non-symmetric similarity functions, as well as with mixed and incomplete data. The proposal obtains very good results on the SABM data, and over repository databases
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality
reduction, with well-documented merits in various applications involving
high-dimensional data, including computer vision, preference measurement, and
bioinformatics. In this context, the fresh look advocated here permeates
benefits from variable selection and compressive sampling, to robustify PCA
against outliers. A least-trimmed squares estimator of a low-rank bilinear
factor analysis model is shown closely related to that obtained from an
-(pseudo)norm-regularized criterion encouraging sparsity in a matrix
explicitly modeling the outliers. This connection suggests robust PCA schemes
based on convex relaxation, which lead naturally to a family of robust
estimators encompassing Huber's optimal M-class as a special case. Outliers are
identified by tuning a regularization parameter, which amounts to controlling
sparsity of the outlier matrix along the whole robustification path of (group)
least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its
neat ties to robust statistics, the developed outlier-aware PCA framework is
versatile to accommodate novel and scalable algorithms to: i) track the
low-rank signal subspace robustly, as new data are acquired in real time; and
ii) determine principal components robustly in (possibly) infinite-dimensional
feature spaces. Synthetic and real data tests corroborate the effectiveness of
the proposed robust PCA schemes, when used to identify aberrant responses in
personality assessment surveys, as well as unveil communities in social
networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin
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