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clearly a function of the learning methodology used for estimating the conditional class
distributions.
As explained in [75], this tradeoff formally justifies the fact that in the pro-cess
of doing dimensionality-cardinality reduction, better estimation of the underlying
observation-class distribution is obtained, in the KLD sense, at the expense of increas-ing
the underlying Bayes error. In particular from these results, by constraining to a
sequence of embedded representations there is one that minimizes the probability of
error, and from the results presented here, the one that achieves the optimal Bayes-estimation
error tradeoff. Hence, it is natural to think that having a rich collection of
feature transformations for X, not necessarily embedded, there is one for which this
tradeoff between “representation quality” and “complexity” achieves an optimal solu-tion,
a solution that is connected with the MPE-SR problem. This is the topic addressed
in the next section.
2.4 Minimum Probability of Error Signal Representa-tion
(MPE-SR)
In this section the above signal representation results will be used to address the MPE-SR
problem. Let us consider again {(xi, yi) : i = 1, ..,N} i.i.d. realizations of the
observation-class (X, Y ) random variables with distribution PX,Y on (X × Y, (FX ×
FY)). In addition, let us consider a family of measurable functions D, where any
f (·) 2 D is defined in X and takes values in a transform space Xf . Every represen-tation
function f (·) induces an empirical distribution ˆ PXf ,Y on (Xf × Y, (Ff × FY)),
based on the training data and an implicit learning approach, and hence the empirical
Bayes rule by
ˆgf (x) = arg max
y2Y
ˆ PXf ,Y (x, y), 8x 2 Xf . (2.9)
17
Object Description
| Title | On optimal signal representation for statistical learning and pattern recognition |
| Author | Silva, Jorge |
| Author email | jorgesil@usc.edu; josilva@ing.uchile.cl |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-06-23 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-21 |
| Advisor (committee chair) | Narayanan, Shrikanth S. |
| Advisor (committee member) |
Kuo, C.-C. Jay Ordóñez, Fernando I. |
| Abstract | This work presents contributions on two important aspects of the role of signal representation in statistical learning problems, in the context of deriving new methodologies and representations for speech recognition and the estimation of information theoretic quantities.; The first topic focuses on the problem of optimal filter bank selection using Wavelet Packets (WPs) for speech recognition applications. We propose new results to show an estimation-approximation error tradeoff across sequence of embedded representations. These results were used to formulate the minimum probability of error signal representation (MPE-SR) problem as a complexity regularization criterion. Restricting this criterion to the filter bank selection, algorithmic solutions are provided by exploring the dyadic tree-structure of WPs. These solutions are stipulated in terms of a set of conditional independent assumptions for the acoustic observation process, in particular, a Markov tree property across the indexed structure of WPs. In the technical side, this work presents contributions on the extension of minimum cost tree pruning algorithms and their properties to affine tree functionals. For the experimental validation, a phone classification task ratifies the goodness of Wavelet Packets as an analysis scheme for non-stationary time-series processes, and the effectiveness of the MPE-SR to provide cost effective discriminative filter bank solution for pattern recognition.; The second topic addresses the problem of data-dependent partitions for the estimation of mutual information and Kullback-Leibler divergence (KLD). This work proposes general histogram-based estimates considering non-product data-driven partition schemes. The main contribution is the stipulation of sufficient conditions to make these histogram-based constructions strongly consistent for both problems. The sufficient conditions consider combinatorial complexity indicator for partition families and the use of large deviation type of inequalities (Vapnik-Chervonenkis inequalities). On the application side, two emblematic data-dependent constructions are derived from this result, one based on statistically equivalent blocks and the other, on a tree-structured vector quantization scheme. A range of design values was stipulated to guarantee strongly consistency estimates for both framework. Furthermore, experimental results under controlled settings demonstrate the superiority of these data-driven techniques in terms of a bias-variance analysis when compared to conventional product histogram-based and kernel plug-in estimates. |
| Keyword | signal representation in statistical learning; Bayes decision theory; basis selection; tree-structured bases and Wavelet packet (WP); complexity regularization; minimum cost tree pruning; family pruning problem; mutual information estimation; divergence estimation; data-dependent partitions; statistical learning theory; concentration inequalities; tree-structured vector quantization. |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1684 |
| Contributing entity | University of Southern California |
| Rights | Silva, Jorge |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Silva-2450 |
| Archival file | uscthesesreloadpub_Volume32/etd-Silva-2450.pdf |
Description
| Title | Page 30 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | clearly a function of the learning methodology used for estimating the conditional class distributions. As explained in [75], this tradeoff formally justifies the fact that in the pro-cess of doing dimensionality-cardinality reduction, better estimation of the underlying observation-class distribution is obtained, in the KLD sense, at the expense of increas-ing the underlying Bayes error. In particular from these results, by constraining to a sequence of embedded representations there is one that minimizes the probability of error, and from the results presented here, the one that achieves the optimal Bayes-estimation error tradeoff. Hence, it is natural to think that having a rich collection of feature transformations for X, not necessarily embedded, there is one for which this tradeoff between “representation quality” and “complexity” achieves an optimal solu-tion, a solution that is connected with the MPE-SR problem. This is the topic addressed in the next section. 2.4 Minimum Probability of Error Signal Representa-tion (MPE-SR) In this section the above signal representation results will be used to address the MPE-SR problem. Let us consider again {(xi, yi) : i = 1, ..,N} i.i.d. realizations of the observation-class (X, Y ) random variables with distribution PX,Y on (X × Y, (FX × FY)). In addition, let us consider a family of measurable functions D, where any f (·) 2 D is defined in X and takes values in a transform space Xf . Every represen-tation function f (·) induces an empirical distribution ˆ PXf ,Y on (Xf × Y, (Ff × FY)), based on the training data and an implicit learning approach, and hence the empirical Bayes rule by ˆgf (x) = arg max y2Y ˆ PXf ,Y (x, y), 8x 2 Xf . (2.9) 17 |
