Dimensional reduction

Appendix A: An Overview on Time Series Data Mining includes Introduction, Similarity Search in Time Series Data, Featurebased Dimensionality Reduction, Discretization, Other Time Series Data Mining Tasks, Conclusions.
37p cocacola_10 08122015 7 1 Download

It is more than a century since Karl Pearson invented the concept of Principal Component Analysis (PCA). Nowadays, it is a very useful tool in data analysis in many fields. PCA is the technique of dimensionality reduction, which transforms data in the highdimensional space to space of lower dimensions. The advantages of this subspace are numerous. First of all, the reduced dimension has the effect of retaining the most of the useful information while reducing noise and other undesirable artifacts. Secondly, the time and memory that used in data processing are smaller.
0p qsczaxewd 25092012 32 6 Download

Reduction of the singularities of codimension one singular foliations in dimension three By Felipe Cano Contents 0. Introduction 1. Blowingup singular foliations 1.1. Adapted singular foliations 1.2. Permissible centers 1.3. Vertical invariants 1.4. First properties of presimple singularities 2. Global strategy 2.1. Reduction to presimple singularities. Statement 2.2. Good points. Bad points. Equireduction 2.3. Finiteness of bad points 2.4. The inﬂuency locus 2.5. The local control theorem 2.6. Destroying cycles 2.7. Global criteria of blowingup 3. Local control 3.1.
106p tuanloccuoi 04012013 19 6 Download

A threedimensional macroporous Cu/SnO2 composite anode sheet prepared via a novel method was prepared via a novel method that is based on selective reduction of metal oxides at appropriate temperatures. SnO2 particles were imbedded on the Cu particles within the threedimensionally interconnected Cu substrate.
6p nguyenphong201 09072016 11 1 Download

This is a reference book for those practicing or otherwise having an interest in design for manufacturability (DFM). DFM principles and guidelines are many; no one person should be expected to remember them all nor the detailed information, such as suggested dimensional tolerances, process limits, expected surface finish values, or other details, of each manufacturing process. It is expected that those involved will keep this book handy for reference when needed.
1292p nguyenvanminh512 27012013 68 36 Download

This book addresses different aspects of the research field and a wide range of topics in speech signal processing, speech recognition and language processing. The chapters are divided in three different sections: Speech Signal Modeling, Speech Recognition and Applications. The chapters in the first section cover some essential topics in speech signal processing used for building speech recognition as well as for speech synthesis systems: speech feature enhancement, speech feature vector dimensionality reduction, segmentation of speech frames into phonetic segments. ...
0p bi_bi1 13072012 29 6 Download

We establish an exact relation between selfavoiding branched polymers in D + 2 continuum dimensions and the hardcore continuum gas at negative activity in D dimensions. We review conjectures and results on critical exponents for D + 2 = 2, 3, 4 and show that they are corollaries of our result. We explain the connection (ﬁrst proposed by Parisi and Sourlas) between branched polymers in D + 2 dimensions and the YangLee edge singularity in D dimensions.
22p tuanloccuoi 04012013 22 6 Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: ITemplatefree Synthesis of Onedimensional Cobalt Nanostructures by Hydrazine Reduction Route
5p dauphong14 13022012 21 4 Download

Tuyển tập các báo cáo nghiên cứu về lâm nghiệp được đăng trên tạp chí lâm nghiệp Original article đề tài:"Reduction of wood hygroscopicity and associated dimensional response by repeated humidity cycles"
10p toshiba5 19092011 18 2 Download

We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different realworld datasets, we ﬁnd IDMLIT, a semisupervised metric learning algorithm to be the most effective.
5p hongdo_1 12042013 17 2 Download

Six new benzopyran derivatives were synthesized by reduction reaction and Michael reaction from malloapelta B.
5p uocvong04 24092015 5 2 Download

Many classical integrable systems (like the Euler, Lagrange and Kowalewski tops or the Neumann system) as well as finite dimensional reductions of many integrable PDEs share the property of being algebraically completely integrable systems4. This means that they are completely integrable Hamiltonian systems in the usual sense and, moreover, their complexified invariant tori are open subsets of complex Abelian tori on which the complexified flow is linear. To such systems the powerful algebrogeometrical techniques may be applied...
260p maket1311 16102012 14 1 Download

This book collects the lecture notes of two courses and one minicourse held in a winter school in Bologna in January 2005. The aim of this school was to popularize techniques of geometric measure theory among researchers and PhD students in hyperbolic differential equations. Though initially developed in the context of the calculus of variations, many of these techniques have proved to be quite powerful for the treatment of some hyperbolic problems.
138p coeus75 29012013 10 1 Download

This paper presents a method for inducing the parts of speech of a language and partofspeech labels for individual words from a large text corpus. Vector representations for the partofspeech of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. The method classifies both ambiguous and unambiguous words correctly with high accuracy. ...
8p bunmoc_1 20042013 16 1 Download

Main Receive Aperture and Analog Beamforming Data to be Processed The Processing Needs and Major Issues Temporal DOF Reduction Adaptive Filtering with Needed and SampleSupportable DOF and Embedded CFAR Processing 70.6 ScanToScan TrackBeforeDetect Processing 70.7 RealTime Nonhomogeneity Detection and Sample Conditioning and Selection 70.8 Space or SpaceRange Adaptive PreSuppression of Jammers 70.9 A STAP Example with a Revisit to Analog Beamforming 70.
16p longmontran 18012010 65 10 Download

Distance, range number, bilaterial tolerance Width, range number Constant Helix diameter Dimensional operator Young's modulus Error using n applications of Simpson's rule The /th exponent Function The /th derivative of function / Fundamental dimension of force, fractional reduction of interval of uncertainty Function Function, ordinate spacing Index Second area moment, value of integral Approximate value of integral using i applications of Simpson's rule Spring rate
21p thachsaudoi 22122009 68 6 Download

CHAPTER 11 MINIMIZING ENGINEERING EFFORT Charles R. Mischke, Ph.D., P.E. Professor Emeritus of Mechanical Engineering Iowa State University Ames, Iowa 11.1 INTRODUCTION/11.2 11.2 REDUCING THE NUMBER OF EXPERIMENTS /11.3 11.3 SIMILITUDE/11.7 11.4 OPTIMALITY/11.9 11.5 QUADRATURE/11.13 11.6 CHECKING/11.15 REFERENCES/11.
21p hadalabo 29092010 47 5 Download

Tham khảo sách 'principal component analysis – engineering applications', kỹ thuật  công nghệ, cơ khí  chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
230p qsczaxewd 25092012 20 5 Download

The Fourier transform of a C ∞ function, f , with compact support on a real reductive Lie group G is given by a collection of operators φ(P, σ, λ) := π P (σ, λ)(f ) for a suitable family of representations of G, which depends on a family, indexed by P in a ﬁnite set of parabolic subgroups of G, of pairs of parameters (σ, λ), σ varying in a set of discrete series, λ lying in a complex ﬁnite dimensional vector space. The π P (σ, λ) are generalized principal series, induced from P . It is...
44p noel_noel 17012013 26 4 Download

One of the major problems of Kmeans is that one must use dense vectors for its centroids, and therefore it is infeasible to store such huge vectors in memory when the feature space is highdimensional. We address this issue by using feature hashing (Weinberger et al., 2009), a dimensionreduction technique, which can reduce the size of dense vectors while retaining sparsity of sparse vectors.
5p hongdo_1 12042013 22 4 Download