4,571 research outputs found
Delete or merge regressors for linear model selection
We consider a problem of linear model selection in the presence of both
continuous and categorical predictors. Feasible models consist of subsets of
numerical variables and partitions of levels of factors. A new algorithm called
delete or merge regressors (DMR) is presented which is a stepwise backward
procedure involving ranking the predictors according to squared t-statistics
and choosing the final model minimizing BIC. In the article we prove
consistency of DMR when the number of predictors tends to infinity with the
sample size and describe a simulation study using a pertaining R package. The
results indicate significant advantage in time complexity and selection
accuracy of our algorithm over Lasso-based methods described in the literature.
Moreover, a version of DMR for generalized linear models is proposed
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs
A Fast Minimal Infrequent Itemset Mining Algorithm
A novel fast algorithm for finding quasi identifiers in large datasets is
presented. Performance measurements on a broad range of datasets demonstrate
substantial reductions in run-time relative to the state of the art and the
scalability of the algorithm to realistically-sized datasets up to several
million records
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout
different days, and can directly affect the person's health. In this work, we
address the recognition of routine related days. To do so, we rely on
egocentric images, which are recorded by a wearable camera and allow to monitor
the life of the user from a first-person view perspective. We propose an
unsupervised model that identifies routine related days, following an outlier
detection approach. We test the proposed framework over a total of 72 days in
the form of photo-streams covering around 2 weeks of the life of 5 different
camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted
F-Score for all the users. Thus, we show that our framework is able to
recognise routine related days and opens the door to the understanding of the
behaviour of people
Multiscale 3D Shape Analysis using Spherical Wavelets
©2005 Springer. The original publication is available at www.springerlink.com:
http://dx.doi.org/10.1007/11566489_57DOI: 10.1007/11566489_57Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data
Classic and Bayesian Tree-Based Methods
Tree-based methods are nonparametric techniques and machine-learning methods for data prediction and exploratory modeling. These models are one of valuable and powerful tools among data mining methods and can be used for predicting different types of outcome (dependent) variable: (e.g., quantitative, qualitative, and time until an event occurs (survival data)). Tree model is called classification tree/regression tree/survival tree based on the type of outcome variable. These methods have some advantages over against traditional statistical methods such as generalized linear models (GLMs), discriminant analysis, and survival analysis. Some of these advantages are: without requiring to determine assumptions about the functional form between outcome variable and predictor (independent) variables, invariant to monotone transformations of predictor variables, useful for dealing with nonlinear relationships and high-order interactions, deal with different types of predictor variable, ease of interpretation and understanding results without requiring to have statistical experience, robust to missing values, outliers, and multicollinearity. Several classic and Bayesian tree algorithms are proposed for classification and regression trees, and in this chapter, we provide a review of these algorithms and appropriate criteria for determining the predictive performance of them
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