2,046 research outputs found
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning
problems in domains such as speech, computer vision and natural language
processing. In this paper, we consider a novel class of matrix and
tensor-valued features, which can be pre-trained using unlabeled samples. We
present efficient algorithms for extracting discriminative information, given
these pre-trained features and labeled samples for any related task. Our class
of features are based on higher-order score functions, which capture local
variations in the probability density function of the input. We establish a
theoretical framework to characterize the nature of discriminative information
that can be extracted from score-function features, when used in conjunction
with labeled samples. We employ efficient spectral decomposition algorithms (on
matrices and tensors) for extracting discriminative components. The advantage
of employing tensor-valued features is that we can extract richer
discriminative information in the form of an overcomplete representations.
Thus, we present a novel framework for employing generative models of the input
for discriminative learning.Comment: 29 page
Visualizing and Understanding Sum-Product Networks
Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this paper we explore and exploit the inner
representations learned by SPNs. We do this with a threefold aim: first we want
to get a better understanding of the inner workings of SPNs; secondly, we seek
additional ways to evaluate one SPN model and compare it against other
probabilistic models, providing diagnostic tools to practitioners; lastly, we
want to empirically evaluate how good and meaningful the extracted
representations are, as in a classic Representation Learning framework. In
order to do so we revise their interpretation as deep neural networks and we
propose to exploit several visualization techniques on their node activations
and network outputs under different types of inference queries. To investigate
these models as feature extractors, we plug some SPNs, learned in a greedy
unsupervised fashion on image datasets, in supervised classification learning
tasks. We extract several embedding types from node activations by filtering
nodes by their type, by their associated feature abstraction level and by their
scope. In a thorough empirical comparison we prove them to be competitive
against those generated from popular feature extractors as Restricted Boltzmann
Machines. Finally, we investigate embeddings generated from random
probabilistic marginal queries as means to compare other tractable
probabilistic models on a common ground, extending our experiments to Mixtures
of Trees.Comment: Machine Learning Journal paper (First Online), 24 page
Supervised learning of short and high-dimensional temporal sequences for life science measurements
The analysis of physiological processes over time are often given by
spectrometric or gene expression profiles over time with only few time points
but a large number of measured variables. The analysis of such temporal
sequences is challenging and only few methods have been proposed. The
information can be encoded time independent, by means of classical expression
differences for a single time point or in expression profiles over time.
Available methods are limited to unsupervised and semi-supervised settings. The
predictive variables can be identified only by means of wrapper or
post-processing techniques. This is complicated due to the small number of
samples for such studies. Here, we present a supervised learning approach,
termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a
supervised mapping of the temporal sequences onto a low dimensional grid. We
utilize a hidden markov model (HMM) to account for the time domain and
relevance learning to identify the relevant feature dimensions most predictive
over time. The learned mapping can be used to visualize the temporal sequences
and to predict the class of a new sequence. The relevance learning permits the
identification of discriminating masses or gen expressions and prunes
dimensions which are unnecessary for the classification task or encode mainly
noise. In this way we obtain a very efficient learning system for temporal
sequences. The results indicate that using simultaneous supervised learning and
metric adaptation significantly improves the prediction accuracy for
synthetically and real life data in comparison to the standard techniques. The
discriminating features, identified by relevance learning, compare favorably
with the results of alternative methods. Our method permits the visualization
of the data on a low dimensional grid, highlighting the observed temporal
structure
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Hierarchical text classification has many real-world applications. However,
labeling a large number of documents is costly. In practice, we can use
semi-supervised learning or weakly supervised learning (e.g., dataless
classification) to reduce the labeling cost. In this paper, we propose a path
cost-sensitive learning algorithm to utilize the structural information and
further make use of unlabeled and weakly-labeled data. We use a generative
model to leverage the large amount of unlabeled data and introduce path
constraints into the learning algorithm to incorporate the structural
information of the class hierarchy. The posterior probabilities of both
unlabeled and weakly labeled data can be incorporated with path-dependent
scores. Since we put a structure-sensitive cost to the learning algorithm to
constrain the classification consistent with the class hierarchy and do not
need to reconstruct the feature vectors for different structures, we can
significantly reduce the computational cost compared to structural output
learning. Experimental results on two hierarchical text classification
benchmarks show that our approach is not only effective but also efficient to
handle the semi-supervised and weakly supervised hierarchical text
classification.Comment: Aceepted by 2019 World Wide Web Conference (WWW19
Gene Expression Analysis Methods on Microarray Data a A Review
In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays
- …