Xiaoli Z. Fern

Solving cluster ensemble problems by bipartite graph partitioning

A critical problem in cluster ensemble research is how to combine multiple clusterings to yield a final superior clustering result. Leveraging advanced graph partitioning techniques, we solve this problem by reducing it to a graph partitioning problem. We introduce a new reduction method that constructs a bipartite graph from a given cluster ensemble. The resulting graph models both instances and clusters of the ensemble simultaneously as vertices in the graph. Our approach retains all of the information provided by a given ensemble, allowing the similarity among instances and the similarity among clusters to be considered collectively in forming the final clustering. Further, the resulting graph partitioning problem can be solved efficiently. We empirically evaluate the proposed approach against two commonly used graph formulations and show that it is more robust and achieves comparable or better performance in comparison to its competitors.

Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.

Non-redundant Multi-view Clustering via Orthogonalization

Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. Why commit to one clustering solution while all these alternative clustering views might be interesting to the user. In this paper, we propose a new clustering paradigm for explorative data analysis: find all non-redundant clustering views of the data, where data points of one cluster can belong to different clusters in other views. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches find alternative ways to partition the data by projecting it to a space that is orthogonal to our current solution. The first approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We test our framework on both synthetic and high-dimensional benchmark data sets, and the results show that indeed our approaches were able to discover varied solutions that are interesting and meaningful.

Learning non-redundant codebooks for classifying complex objects

Codebook-based representations are widely employed in the classification of complex objects such as images and documents. Most previous codebook-based methods construct a single codebook via clustering that maps a bag of low-level features into a fixed-length histogram that describes the distribution of these features. This paper describes a simple yet effective framework for learning multiple non-redundant codebooks that produces surprisingly good results. In this framework, each codebook is learned in sequence to extract discriminative information that was not captured by preceding codebooks and their corresponding classifiers. We apply this framework to two application domains: visual object categorization and document classification. Experiments on large classification tasks show substantial improvements in performance compared to a single codebook or codebooks learned in a bagging style.

Time-frequency segmentation of bird song in noisy acoustic environments

Recent work in machine learning considers the problem of identifying bird species from an audio recording. Most methods require segmentation to isolate each syllable of bird call in input audio. Energy-based time-domain segmentation has been successfully applied to low-noise, single-bird recordings. However, audio from automated field recorders contains too much noise for such methods, so a more robust segmentation method is required. We propose a supervised time-frequency audio segmentation method using a Random Forest classifier, to extract syllables of bird call from a noisy signal. When applied to a test data set of 625 field-collected audio segments, our method isolates 93.6% of the acoustic energy of bird song with a false positive rate of 8.6%, outperforming energy thresholding.

Audio Classification of Bird Species: A Statistical Manifold Approach

Our goal is to automatically identify which species of bird is present in an audio recording using supervised learning. Devising effective algorithms for bird species classification is a preliminary step toward extracting useful ecological data from recordings collected in the field. We propose a probabilistic model for audio features within a short interval of time, then derive its Bayes risk-minimizing classifier, and show that it is closely approximated by a nearest-neighbor classifier using Kullback-Leibler divergence to compare histograms of features. We note that feature histograms can be viewed as points on a statistical manifold, and KL divergence approximates geodesic distances defined by the Fisher information metric on such manifolds. Motivated by this fact, we propose the use of another approximation to the Fisher information metric, namely the Hellinger metric. The proposed classifiers achieve over 90% accuracy on a data set containing six species of bird, and outperform support vector machines.

The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment

Birds have been widely used as biological indicators for ecological research. They respond quickly to environmental changes and can be used to infer about other organisms (e.g., insects they feed on). Traditional methods for collecting data about birds involves costly human effort. A promising alternative is acoustic monitoring. There are many advantages to recording audio of birds compared to human surveys, including increased temporal and spatial resolution and extent, applicability in remote sites, reduced observer bias, and potentially lower cost. However, it is an open problem for signal processing and machine learning to reliably identify bird sounds in real-world audio data collected in an acoustic monitoring scenario. Some of the major challenges include multiple simultaneously vocalizing birds, other sources of non-bird sound (e.g., buzzing insects), and background noise like wind, rain, and motor vehicles.

Prune-and-Score: Learning for Greedy Coreference Resolution

We propose a novel search-based approach for greedy coreference resolution, where the mentions are processed in order and added to previous coreference clusters. Our method is distinguished by the use of two functions to make each coreference decision: a pruning function that prunes bad coreference decisions from further consideration, and a scoring function that then selects the best among the remaining decisions. Our framework reduces learning of these functions to rank learning, which helps leverage powerful off-the-shelf rank-learners. We show that our Prune-and-Score approach is superior to using a single scoring function to make both decisions and outperforms several state-of-the-art approaches on multiple benchmark corpora including OntoNotes.

Optimization of pH and nitrogen for enhanced hydrogen production by Synechocystis sp. PCC 6803 via statistical and machine learning methods.

The nitrogen (N) concentration and pH of culture media were optimized for increased fermentative hydrogen (H2) production from the cyanobacterium, Synechocystis sp. PCC 6803. The optimization was conducted using two procedures, response surface methodology (RSM), which is commonly used, and a memory‐based machine learning algorithm, Q2, which has not been used previously in biotechnology applications. Both RSM and Q2 were successful in predicting optimum conditions that yielded higher H2 than the media reported by Burrows et al., Int J Hydrogen Energy. 2008;33:6092–6099 optimized for N, S, and C (called EHB‐1 media hereafter), which itself yielded almost 150 times more H2 than Synechocystis sp. PCC 6803 grown on sulfer‐free BG‐11 media. RSM predicted an optimum N concentration of 0.63 mM and pH of 7.77, which yielded 1.70 times more H2 than EHB‐1 media when normalized to chlorophyll concentration (0.68 ± 0.43 μmol H2 mg Chl−1 h−1) and 1.35 times more when normalized to optical density (1.62 ± 0.09 nmol H2 OD730−1 h−1). Q2 predicted an optimum of 0.36 mM N and pH of 7.88, which yielded 1.94 and 1.27 times more H2 than EHB‐1 media when normalized to chlorophyll concentration (0.77 ± 0.44 μmol H2 mg Chl−1 h−1) and optical density (1.53 ± 0.07 nmol H2 OD730−1 h−1), respectively. Both optimization methods have unique benefits and drawbacks that are identified and discussed in this study.

A Syllable-Level Probabilistic Framework for Bird Species Identification

In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregating frame level features within a syllable. We characterize each syllable as a probability distribution of its frame level features. The independent frame independent syllable (IFIS) model allows us to distinguish syllables whose feature distributions are different from one another. The Markov chain frame independent syllable (MCFIS) model is introduced for scenarios where the temporal structure within the syllable provides significant amount of discriminative information. We derive the Bayes risk minimizing classifier for each model and show that it can be approximated as a nearest neighbour classifier. Our experiments indicate that the IFIS and MCFIS models achieve 88.26% and 90.61% correct classification rates, respectively, while the equivalent SVM implementation achieves 86.15%.