3,272 research outputs found
Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation
There is a growing interest in computer science, engineering, and mathematics
for modeling signals in terms of union of subspaces and manifolds. Subspace
segmentation and clustering of high dimensional data drawn from a union of
subspaces are especially important with many practical applications in computer
vision, image and signal processing, communications, and information theory.
This paper presents a clustering algorithm for high dimensional data that comes
from a union of lower dimensional subspaces of equal and known dimensions. Such
cases occur in many data clustering problems, such as motion segmentation and
face recognition. The algorithm is reliable in the presence of noise, and
applied to the Hopkins 155 Dataset, it generates the best results to date for
motion segmentation. The two motion, three motion, and overall segmentation
rates for the video sequences are 99.43%, 98.69%, and 99.24%, respectively
CUR Decompositions, Similarity Matrices, and Subspace Clustering
A general framework for solving the subspace clustering problem using the CUR
decomposition is presented. The CUR decomposition provides a natural way to
construct similarity matrices for data that come from a union of unknown
subspaces . The similarity
matrices thus constructed give the exact clustering in the noise-free case.
Additionally, this decomposition gives rise to many distinct similarity
matrices from a given set of data, which allow enough flexibility to perform
accurate clustering of noisy data. We also show that two known methods for
subspace clustering can be derived from the CUR decomposition. An algorithm
based on the theoretical construction of similarity matrices is presented, and
experiments on synthetic and real data are presented to test the method.
Additionally, an adaptation of our CUR based similarity matrices is utilized
to provide a heuristic algorithm for subspace clustering; this algorithm yields
the best overall performance to date for clustering the Hopkins155 motion
segmentation dataset.Comment: Approximately 30 pages. Current version contains improved algorithm
and numerical experiments from the previous versio
Suicide Rate Predictions In Pakistan By using Neural Networks
Suicide is the understudied subject in Pakistan that is a cause of death all over the world. Seventy-fivepercent of suicide occurs in LMIC.In Pakistan information about suicide is limited. The study is about tofind the number of suicide from major cities of Pakistan and then predict the number of suicides by usingNeural Networks Algorithm. About 24639 cases were found in our research from 2001-18 in majorcities of Pakistan. Hanging and poisoning were the most common methods of suicide. The peak age ofsuicide committers was 20-35 included males and females. The lowest number of suicide was inBahawalpur (130 from 2001 to 2018) and the Highest was in Lahore (5925 from 2001 to 2018)
THE PROCESSES OF MANAGEMENT ACCOUNTING CHANGE IN LIBYAN PRIVATISED COMPANIES: AN INSTITUTIONAL PERSPECTIVE
Abstract:
This study explains the management accounting process in two privatised Libyan manufacturing companies. In addition, it investigates the perception of managers regarding the emergence of new management accounting systems and/or practices. Moreover, it explores the effect of institutional factors on management accounting systems. The research is based on a case study of two privatised Libyan companies. It uses triangulation of data collection methods and multiple sources of evidence, including interviews, observation and documentation. Using an institutional framework from new institutional sociology (NIS), old institutional economics (OIE) and power mobilisation are used to help clarify the processes of change in Libyan companies. The hybrid-institutional framework utilised in this study has aided in explaining, interpreting and understanding effects which have occurred within the organisations, which involve rules and routines and/or external-organisation, including terms of coercive, mimetic and normative isomorphism.
The position as mentioned above cannot be described in terms of quantitative approaches. So the main reason behind the selection of a qualitative approach for this was that the important aim of the qualitative approach is to provide an in-depth understanding of particular phenomena, such as management accounting change. Also, the case study strategy has been chosen from among qualitative strategies; this was appropriate as the researcher wished to provide a fuller understanding of the topics of the research. Triangulation data collection methods have been drawn on. The case studies were carried out during two stages of data collection in 2008-2009. The researcher relied on multiple sources of evidence, including interviews, observations and documents and archival records. Semi-structured interviews were adopted. In this context, the researcher had a list of themes and questions, as well as responses of interviewees which were recorded. Participant observation was converged as it is related to qualitative approach and case study strategy as well.
The results of the case studies showed that the objectives of the companies have changed completely from social to economic. The study found that there were institutional factors which affected management accounting systems and practice before, during and after the privatisation process. Also, the case studies affirmed that the changes were incremental or evolutionary. Furthermore, the findings showed that there is no current revolutionary change within the management accounting systems and practices in the companies under the study. It was also found that there was resistance to change when the company attempted to introduce an Information Technology (IT) system
Reduced row echelon form and non-linear approximation for subspace segmentation and high-dimensional data clustering
Given a set of data W={w1,…,wN}∈RD drawn from a union of subspaces, we focus on determining a nonlinear model of the form U=⋃i∈ISi, where {Si⊂RD}i∈I is a set of subspaces, that is nearest to W. The model is then used to classify W into clusters. Our approach is based on the binary reduced row echelon form of data matrix, combined with an iterative scheme based on a non-linear approximation method. We prove that, in absence of noise, our approach can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace Si. We provide a comprehensive analysis of our theory and determine its limitations and strengths in presence of outliers and noise
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