817,834 research outputs found
A Comparison of Multi-instance Learning Algorithms
Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms.
This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems
Using relevance feedback in expert search
In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their "expertise need" by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks
Setting per-field normalisation hyper-parameters for the named-page finding search task
Per-field normalisation has been shown to be effective for Web search tasks, e.g. named-page finding. However, per-field normalisation also suffers from having hyper-parameters to tune on a per-field basis. In this paper, we argue that the purpose of per-field normalisation is to adjust the linear relationship between field length and term frequency. We experiment with standard Web test collections, using three document fields, namely the body of the document, its title, and the anchor text of its incoming links. From our experiments, we find that across different collections, the linear correlation values, given by the optimised hyper-parameter settings, are proportional to the maximum negative linear correlation. Based on this observation, we devise an automatic method for setting the per-field normalisation hyper-parameter values without the use of relevance assessment for tuning. According to the evaluation results, this method is shown to be effective for the body and title fields. In addition, the difficulty in setting the per-field normalisation hyper-parameter for the anchor text field is explained
Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons
The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations.
We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
We consider statistical as well as algorithmic aspects of solving large-scale
least-squares (LS) problems using randomized sketching algorithms. For a LS
problem with input data , sketching algorithms use a sketching matrix, with . Then, rather than solving the LS problem using the
full data , sketching algorithms solve the LS problem using only the
sketched data . Prior work has typically adopted an algorithmic
perspective, in that it has made no statistical assumptions on the input
and , and instead it has been assumed that the data are fixed and
worst-case (WC). Prior results show that, when using sketching matrices such as
random projections and leverage-score sampling algorithms, with ,
the WC error is the same as solving the original problem, up to a small
constant. From a statistical perspective, we typically consider the
mean-squared error performance of randomized sketching algorithms, when data
are generated according to a statistical model , where is a noise process. We provide a rigorous
comparison of both perspectives leading to insights on how they differ. To do
this, we first develop a framework for assessing algorithmic and statistical
aspects of randomized sketching methods. We then consider the statistical
prediction efficiency (PE) and the statistical residual efficiency (RE) of the
sketched LS estimator; and we use our framework to provide upper bounds for
several types of random projection and random sampling sketching algorithms.
Among other results, we show that the RE can be upper bounded when while the PE typically requires the sample size to be substantially
larger. Lower bounds developed in subsequent results show that our upper bounds
on PE can not be improved.Comment: 27 pages, 5 figure
Rank Centrality: Ranking from Pair-wise Comparisons
The question of aggregating pair-wise comparisons to obtain a global ranking
over a collection of objects has been of interest for a very long time: be it
ranking of online gamers (e.g. MSR's TrueSkill system) and chess players,
aggregating social opinions, or deciding which product to sell based on
transactions. In most settings, in addition to obtaining a ranking, finding
`scores' for each object (e.g. player's rating) is of interest for
understanding the intensity of the preferences.
In this paper, we propose Rank Centrality, an iterative rank aggregation
algorithm for discovering scores for objects (or items) from pair-wise
comparisons. The algorithm has a natural random walk interpretation over the
graph of objects with an edge present between a pair of objects if they are
compared; the score, which we call Rank Centrality, of an object turns out to
be its stationary probability under this random walk. To study the efficacy of
the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model
(equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which
each object has an associated score which determines the probabilistic outcomes
of pair-wise comparisons between objects. In terms of the pair-wise marginal
probabilities, which is the main subject of this paper, the MNL model and the
BTL model are identical. We bound the finite sample error rates between the
scores assumed by the BTL model and those estimated by our algorithm. In
particular, the number of samples required to learn the score well with high
probability depends on the structure of the comparison graph. When the
Laplacian of the comparison graph has a strictly positive spectral gap, e.g.
each item is compared to a subset of randomly chosen items, this leads to
dependence on the number of samples that is nearly order-optimal.Comment: 45 pages, 3 figure
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