21,821 research outputs found
Heuristic Ternary Error-Correcting Output Codes Via Weight Optimization and Layered Clustering-Based Approach
One important classifier ensemble for multiclass classification problems is
Error-Correcting Output Codes (ECOCs). It bridges multiclass problems and
binary-class classifiers by decomposing multiclass problems to a serial
binary-class problems. In this paper, we present a heuristic ternary code,
named Weight Optimization and Layered Clustering-based ECOC (WOLC-ECOC). It
starts with an arbitrary valid ECOC and iterates the following two steps until
the training risk converges. The first step, named Layered Clustering based
ECOC (LC-ECOC), constructs multiple strong classifiers on the most confusing
binary-class problem. The second step adds the new classifiers to ECOC by a
novel Optimized Weighted (OW) decoding algorithm, where the optimization
problem of the decoding is solved by the cutting plane algorithm. Technically,
LC-ECOC makes the heuristic training process not blocked by some difficult
binary-class problem. OW decoding guarantees the non-increase of the training
risk for ensuring a small code length. Results on 14 UCI datasets and a music
genre classification problem demonstrate the effectiveness of WOLC-ECOC
Modular invariants and singularity indices of hyperelliptic fibrations
The modular invariants of a family of semistable curves are the degrees of
the corresponding divisors on the image of the moduli map. The singularity
indices were introduced by G. Xiao to classify singular fibers of hyperelliptic
fibrations and to compute global invariants locally. In the semistable case, we
show that the modular invariants corresponding with the boundary classes are
just the singularity indices. As an application, we show that the formula of
Xiao for relative Chern numbers is the same as that of Cornalba-Harris in the
semistable case.Comment: To appear in Chin. Ann. Math. (B
Learning the kernel matrix by resampling
In this abstract paper, we introduce a new kernel learning method by a
nonparametric density estimator. The estimator consists of a group of
k-centroids clusterings. Each clustering randomly selects data points with
randomly selected features as its centroids, and learns a one-hot encoder by
one-nearest-neighbor optimization. The estimator generates a sparse
representation for each data point. Then, we construct a nonlinear kernel
matrix from the sparse representation of data. One major advantage of the
proposed kernel method is that it is relatively insensitive to its free
parameters, and therefore, it can produce reasonable results without parameter
tuning. Another advantage is that it is simple. We conjecture that the proposed
method can find its applications in many learning tasks or methods where sparse
representation or kernel matrix is explored. In this preliminary study, we have
applied the kernel matrix to spectral clustering. Our experimental results
demonstrate that the kernel generated by the proposed method outperforms the
well-tuned Gaussian RBF kernel. This abstract paper is used to protect the
idea, full versions will be updated later
Multilayer bootstrap network for unsupervised speaker recognition
We apply multilayer bootstrap network (MBN), a recent proposed unsupervised
learning method, to unsupervised speaker recognition. The proposed method first
extracts supervectors from an unsupervised universal background model, then
reduces the dimension of the high-dimensional supervectors by multilayer
bootstrap network, and finally conducts unsupervised speaker recognition by
clustering the low-dimensional data. The comparison results with 2 unsupervised
and 1 supervised speaker recognition techniques demonstrate the effectiveness
and robustness of the proposed method
Linear Regression for Speaker Verification
This paper presents a linear regression based back-end for speaker
verification. Linear regression is a simple linear model that minimizes the
mean squared estimation error between the target and its estimate with a closed
form solution, where the target is defined as the ground-truth indicator
vectors of utterances. We use the linear regression model to learn speaker
models from a front-end, and verify the similarity of two speaker models by a
cosine similarity scoring classifier. To evaluate the effectiveness of the
linear regression model, we construct three speaker verification systems that
use the Gaussian mixture model and identity-vector (GMM/i-vector) front-end,
deep neural network and i-vector (DNN/i-vector) front-end, and deep vector
(d-vector) front-end as their front-ends, respectively. Our empirical
comparison results on the NIST speaker recognition evaluation data sets show
that the proposed method outperforms within-class covariance normalization,
linear discriminant analysis, and probabilistic linear discriminant analysis,
given any of the three front-ends
Learning Deep Representations By Distributed Random Samplings
In this paper, we propose an extremely simple deep model for the unsupervised
nonlinear dimensionality reduction -- deep distributed random samplings, which
performs like a stack of unsupervised bootstrap aggregating. First, its network
structure is novel: each layer of the network is a group of mutually
independent -centers clusterings. Second, its learning method is extremely
simple: the centers of each clustering are only randomly selected
examples from the training data; for small-scale data sets, the centers are
further randomly reconstructed by a simple cyclic-shift operation. Experimental
results on nonlinear dimensionality reduction show that the proposed method can
learn abstract representations on both large-scale and small-scale problems,
and meanwhile is much faster than deep neural networks on large-scale problems
Multilayer bootstrap networks
Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear
network from bottom up for unsupervised nonlinear dimensionality reduction.
Each layer of the network is a nonparametric density estimator. It consists of
a group of k-centroids clusterings. Each clustering randomly selects data
points with randomly selected features as its centroids, and learns a one-hot
encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric
density estimator at each layer projects the input data space to a
uniformly-distributed discrete feature space, where the similarity of two data
points in the discrete feature space is measured by the number of the nearest
centroids they share in common. The multilayer network gradually reduces the
nonlinear variations of data from bottom up by building a vast number of
hierarchical trees implicitly on the original data space. Theoretically, the
estimation error caused by the nonparametric density estimator is proportional
to the correlation between the clusterings, both of which are reduced by the
randomization steps.Comment: accepted for publication by Neural Network
Deep Ad-hoc Beamforming
Far-field speech processing is an important and challenging problem. In this
paper, we propose \textit{deep ad-hoc beamforming}, a deep-learning-based
multichannel speech enhancement framework based on ad-hoc microphone arrays, to
address the problem. It contains three novel components. First, it combines
\textit{ad-hoc microphone arrays} with deep-learning-based multichannel speech
enhancement, which reduces the probability of the occurrence of far-field
acoustic environments significantly. Second, it groups the microphones around
the speech source to a local microphone array by a supervised channel selection
framework based on deep neural networks. Third, it develops a simple time
synchronization framework to synchronize the channels that have different time
delay. Besides the above novelties and advantages, the proposed model is also
trained in a single-channel fashion, so that it can easily employ new
development of speech processing techniques. Its test stage is also flexible in
incorporating any number of microphones without retraining or modifying the
framework. We have developed many implementations of the proposed framework and
conducted an extensive experiment in scenarios where the locations of the
speech sources are far-field, random, and blind to the microphones. Results on
speech enhancement tasks show that our method outperforms its counterpart that
works with linear microphone arrays by a considerable margin in both diffuse
noise reverberant environments and point source noise reverberant environments
An Investigation of Universal Background Sparse Coding Based Speaker Verification on TIMIT
In this paper, we propose a universal background model, named universal
background sparse coding (UBSC), for speaker verification. The proposed method
trains an ensemble of clusterings by data resampling, and produces sparse codes
from the clusterings by one-nearest-neighbor optimization plus binarization.
The main advantage of UBSC is that it does not suffer from local minima and
does not make Gaussian assumptions on data distributions. We evaluated UBSC on
a clean speech corpus---TIMIT. We used the cosine similarity and inner product
similarity as the scoring methods of a trial. Experimental results show that
UBSC is comparable to Gaussian mixture model
Cosmological model-independent test of CDM with two-point diagnostic by the observational Hubble parameter data
Aiming at exploring the nature of dark energy (DE), we use forty-three
observational Hubble parameter data (OHD) in the redshift range to make a cosmological model-independent test of the
CDM model with two-point diagnostic. In
CDM model, with equation of state (EoS) , two-point diagnostic
relation is tenable, where is the
present matter density parameter, and is the Hubble parameter divided by
100 . We utilize two methods: the weighted mean and
median statistics to bin the OHD to increase the signal-to-noise ratio of the
measurements. The binning methods turn out to be promising and considered to be
robust. By applying the two-point diagnostic to the binned data, we find that
although the best-fit values of fluctuate as the continuous redshift
intervals change, on average, they are continuous with being constant within 1
confidence interval. Therefore, we conclude that the CDM
model cannot be ruled out.Comment: 14 pages, 7 figures. arXiv admin note: text overlap with
arXiv:1507.0251
- β¦