# The Convergence Rate for a Open image in new window -Functional in Learning Theory

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## Abstract

It is known that in the field of learning theory based on reproducing kernel Hilbert spaces the upper bounds estimate for a Open image in new window -functional is needed. In the present paper, the upper bounds for the Open image in new window -functional on the unit sphere are estimated with spherical harmonics approximation. The results show that convergence rate of the Open image in new window -functional depends upon the smoothness of both the approximated function and the reproducing kernels.

## Keywords

Spherical Harmonic Tikhonov Regularization Cauchy Inequality Jacobi Weight Mercer Kernel## 1. Introduction

It is known that the goal of learning theory is to approximate a function (or some function features) from data samples.

Let Open image in new window be a compact subset of Open image in new window -dimensional Euclidean spaces Open image in new window , Open image in new window . Then, learning theory is to find a function Open image in new window related the input Open image in new window to the output Open image in new window (see [1, 2, 3]). The function Open image in new window is determined by a probability distribution Open image in new window on Open image in new window where Open image in new window is the marginal distribution on Open image in new window and Open image in new window is the condition probability of Open image in new window for a given Open image in new window

Generally, the distribution Open image in new window is known only through a set of sample Open image in new window independently drawn according to Open image in new window . Given a sample Open image in new window , the regression problem based on Support Vector Machine (SVM) learning is to find a function Open image in new window such that Open image in new window is a good estimate of Open image in new window when a new input Open image in new window is provided. The binary classification problem based on SVM learning is to find a function Open image in new window which divides Open image in new window into two parts. Here Open image in new window is often induced by a real-valued function Open image in new window with the form of Open image in new window where Open image in new window if Open image in new window , otherwise, Open image in new window . The functions Open image in new window are often generated from the following Tikhonov regularization scheme (see, e.g., [4, 5, 6, 7, 8, 9]) associated with a reproducing kernel Hilbert space (RKHS) Open image in new window (defined below) and a sample Open image in new window :

where Open image in new window is a positive constant called the regularization parameter and Open image in new window ( Open image in new window ) called Open image in new window -norm SVM loss.

In addition, the Tikhonov regularization scheme involving offset Open image in new window (see, e.g., [4, 10, 11]) can be presented below with a similar way to (1.1)

We are in a position to define reproducing kernel Hilbert space. A function Open image in new window is called a Mercer kernel if it is continuous, symmetric, and positive semidefinite, that is, for any finite set of distinct points Open image in new window , the matrix Open image in new window is positive semidefinite.

The reproducing kernel Hilbert space (RKHS) Open image in new window (see [12]) associated with the Mercer kernel Open image in new window is defined to be the closure of the linear span of the set of functions Open image in new window with the inner product Open image in new window satisfying Open image in new window and the reproducing property

If Open image in new window , then Open image in new window . Denote Open image in new window as the space of continuous function on Open image in new window with the norm Open image in new window . Let Open image in new window Then the reproducing property tells that

It is easy to see that Open image in new window is a subset of Open image in new window We say that Open image in new window is a universal kernel if for any compact subset Open image in new window is dense in Open image in new window (see [13, Page 2652]).

Let Open image in new window be a given discrete set of finite points. Then, we may define an RKHS Open image in new window by the linear span of the set of functions Open image in new window . Then, it is easy to see that Open image in new window and for any Open image in new window there holds Open image in new window

Define Open image in new window and Open image in new window where the minimum is taken over all measurable functions. Then, to estimate the explicit learning rate, one needs to estimate the regularization errors (see, e.g., [4, 7, 9, 14])

The convergence rate of (1.5) is controlled by the Open image in new window -functional (see, e.g., [9])

and (1.6) is controlled by another Open image in new window -functional (see, e.g., [4])

where Open image in new window with

We notice that, on one hand, the Open image in new window -functionals (1.7) and (1.8) are the modifications of the Open image in new window -functional of interpolation theory (see [15]) since the interpolation relation (1.4). On the other hand, they are different from the usual Open image in new window -functionals (see e.g., [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]) since the term Open image in new window However, they have some similar point. For example, if Open image in new window is a universal kernel, Open image in new window is dense in Open image in new window (see e.g., [31]). Moreover, some classical function spaces such as the polynomial spaces (see [2, 32]) and even some Sobolev spaces may be regarded as RKHS (see e.g., [33]).

In learning theory we often require Open image in new window and Open image in new window for some Open image in new window (see e.g., [1, 7, 14]). Many results on this topic have been achieved. With the weighted Durrmeyer operators [8, 9] showed the decay by taking Open image in new window to be the algebraic polynomials kernels on Open image in new window or on the simplex in Open image in new window .

However, in general case, the convergence of Open image in new window -functional (1.8) should also be considered since the offset often has influences on the solution of the learning algorithms (see e.g., [6, 11]). Hence, the purpose of this paper is twofold. One is to provide the convergence rates of (1.7) and (1.8) when Open image in new window is a general Mercer kernel on the unit sphere Open image in new window and Open image in new window The other is how to construct functions of the type of

to obtain the convergence rate of (1.8). The translation networks constructed in [34, 35, 36, 37] have the form of (1.10) and the zonal networks constructed in [38, 39] have the form of (1.10) with Open image in new window . So the methods used by these references may be used here to estimate the convergence rates of (1.7) and (1.8) if one can bound the term Open image in new window

In the present paper, we shall give the convergence rate of (1.7) and (1.8) for a general kernel defined on the unit sphere Open image in new window and Open image in new window with Open image in new window being the usual Lebesgue measure on Open image in new window . If there is a distortion between Open image in new window and Open image in new window the convergence rate of (1.7)-(1.8) in the general case may be obtained according to the way used by [1, 8].

The rest of this paper is organized as follows. In Section 2, we shall restate some notations on spherical harmonics and present the main results. Some useful lemmas dealing with the approximation order for the de la Vallée means of the spherical harmonics, the Gauss integral formula, the Marcinkiewicz-Zygmund with respect to the scattered data obtained by G. Brown and F. Dai and a result on the zonal networks approximation provided by H. N. Mhaskar will be given in Section 3. A kind of weighted norm estimate for the Mercer kernel matrices on the unit sphere will be given in Lemma 3.8. Our main results are proved in the last section.

Throughout the paper, we shall write Open image in new window if there exists a constant Open image in new window such that Open image in new window . We write Open image in new window if Open image in new window and Open image in new window .

## 2. Notations and Results

To state the results of this paper, we need some notations and results on spherical harmonics.

### 2.1. Notations

For integers Open image in new window , Open image in new window , the class of all one variable algebraic polynomials of degree Open image in new window defined on Open image in new window is denoted by Open image in new window , the class of all spherical harmonics of degree Open image in new window will be denoted by Open image in new window , and the class of all spherical harmonics of degree Open image in new window will be denoted by Open image in new window . The dimension of Open image in new window is given by (see [40, Page 65])

and that of Open image in new window is Open image in new window One has the following well-known addition formula (see [41, Page 10, Theorem Open image in new window ]):

where Open image in new window is the degree- Open image in new window generalized Legendre polynomial. The Legendre polynomials are normalized so that Open image in new window and satisfy the orthogonality relations

Define Open image in new window and Open image in new window by taking Open image in new window to be the usual volume element of Open image in new window and the Jacobi weights functions Open image in new window , Open image in new window , Open image in new window , respectively. For any Open image in new window we have the following relation (see [42, Page 312]):

The orthogonal projections Open image in new window of a function Open image in new window on Open image in new window are defined by (see e.g., [43])

where Open image in new window denotes the inner product of Open image in new window and Open image in new window .

### 2.2. Main Results

Let Open image in new window satisfy Open image in new window and Open image in new window . Define

Then, by [44, Chapter 17] we know that Open image in new window is positive semidefinite on Open image in new window and the right of (2.6) is convergence absolutely and uniformly since Open image in new window . Therefore, Open image in new window is a Mercer kernel on Open image in new window By [13, Theorem Open image in new window ] we know that Open image in new window is a universal kernel on Open image in new window . We suppose that there is a constant Open image in new window depending only on Open image in new window such for any Open image in new window

Given a finite set Open image in new window , we denote by Open image in new window the cardinality of Open image in new window . For Open image in new window and Open image in new window we say that a finite subset Open image in new window is an Open image in new window -covering of Open image in new window if

where Open image in new window with Open image in new window being the geodesic distance between Open image in new window and Open image in new window .

Let Open image in new window be an integer, Open image in new window a sequence of real numbers. Define forward difference operators by Open image in new window , Open image in new window , Open image in new window

We say a finite subset Open image in new window is a subset of interpolatory type if for any real numbers Open image in new window there is a Open image in new window such that Open image in new window , Open image in new window This kind of subsets may be found from [45, 46].

Let Open image in new window be the set of all sequence Open image in new window for which Open image in new window and Open image in new window the set of all sequence Open image in new window for which Open image in new window

Let Open image in new window be a real number, Open image in new window Then, we say Open image in new window if there is a function Open image in new window such that

We now give the results of this paper.

Theorem 2.1.

The functions Open image in new window satisfying the conditions of Theorem 2.1 may be found in [39, Page 357].

Corollary 2.2.

Corollary 2.2 shows that the convergence rate of the Open image in new window -functional (1.8) is controlled by the smoothness of both the reproducing kernels and the approximated function Open image in new window .

Theorem 2.3.

where Open image in new window

## 3. Some Lemmas

To prove Theorems 2.1 and 2.3, we need some lemmas. The first one is about the Gauss integral formula and Marcinkiewicz inequalities.

Lemma 3.1 (see [47, 48, 49, 50]).

where Open image in new window the constants of equivalence depending only on Open image in new window , Open image in new window , Open image in new window , and Open image in new window when Open image in new window is small. Here one employs the slight abuse of notation that Open image in new window

The second lemma we shall use is the Nikolskii inequality for the spherical harmonics.

Lemma 3.2 (see [38, 45, 49, 51, 52]).

where the constant Open image in new window depends only on Open image in new window .

We now restate the general approximation frame of the Cesàro means and de la Vallée Poussin means provided by Dai and Ditzian (see [53]).

Lemma 3.3.

Let Open image in new window be a positive measure on Open image in new window . Open image in new window is a sequence of finite-dimensional spaces satisfying the following:

(I) Open image in new window .

(II) Open image in new window is orthogonal to Open image in new window (in Open image in new window ) when Open image in new window

(III) Open image in new window is dense in Open image in new window for all Open image in new window .

(IV) Open image in new window is the collection of the constants.

The Cesàro means Open image in new window of Open image in new window is given by

and Open image in new window is an orthogonal base of Open image in new window in Open image in new window One sets,for a given Open image in new window , Open image in new window and Open image in new window if there exists Open image in new window such that Open image in new window

Let Open image in new window be defined as Open image in new window for Open image in new window and Open image in new window for Open image in new window and is a nonegative and nonincrease function. Open image in new window are the de la Vallée Poussin means defined as

Lemma 3.3 makes the following Lemma 3.4.

Lemma 3.4.

Then, Open image in new window for any Open image in new window and Open image in new window for any Open image in new window . Moreover,

Proof.

By [54, Lemma Open image in new window ] we know Open image in new window for some Open image in new window . Hence, (3.9) holds by (3.7). By [19, Theorem Open image in new window ] we know Open image in new window for Open image in new window Hence, (3.10) holds by (3.7).

Let Open image in new window be a finite set. Then we call Open image in new window an M-Z quadrature measure of order Open image in new window if (3.1) and (3.2) hold for Open image in new window By this definition one knows the finite set Open image in new window in Lemma 3.1 is an M-Z quadrature measure of order Open image in new window .

Define an operator as

Then, we have the following results.

Lemma 3.5 (see [39]).

For a given integer Open image in new window let Open image in new window be an M-Z quadrature measure of order Open image in new window , Open image in new window , Open image in new window an integer, Open image in new window , Open image in new window , where Open image in new window satisfies Open image in new window which satisfies Open image in new window if Open image in new window and Open image in new window if Open image in new window . Open image in new window defined in Lemma 3.3 is a nonnegative and non-increasing function. Let Open image in new window satisfy Open image in new window . Then, for Open image in new window , Open image in new window , where Open image in new window consists of Open image in new window for which the derivative of order Open image in new window ; that is, Open image in new window , belongs to Open image in new window . Then, there is an operator Open image in new window such that

(i)(see [39, Proposition Open image in new window , (b)]). Open image in new window for Open image in new window

where Open image in new window

(ii)(see [39, Theorem Open image in new window ]). Moreover, if one adds an assumption that Open image in new window then, there are constants Open image in new window and Open image in new window such that

Lemma 3.6 (see e.g., [29, Page 230]).

Following Lemma 3.7 deals with the orthogonality of the Legendre polynomials Open image in new window

Lemma 3.7.

Proof.

It may be obtained by (2.2).

Lemma 3.8.

Proof.

By the Parseval equality we have

Let Open image in new window satisfy Open image in new window , Open image in new window . Then, by (3.1)

Equation (3.2) thus holds.

## 4. Proof of the Main Results

We now show Theorems 2.1 and 2.3, respectively.

Proof of Theorem 2.1.

Lemma Open image in new window in [39] gave the following results.

*Let* Open image in new window *,* Open image in new window *,* Open image in new window be an integer, and a sequence of real numbers such Open image in new window *.* Then, there exists Open image in new window such that Open image in new window *,* Open image in new window

Since Open image in new window and Open image in new window we have a Open image in new window such that Open image in new window Hence, Open image in new window and

It follows by (3.9) that

On the other hand, by the definition of Open image in new window and (3.14) we have for Open image in new window that

Take Open image in new window then

Let Open image in new window be the Gamma function. Then, it is well known that Open image in new window Therefore,

Since Open image in new window , we have (2.11) by (4.20). Equation (2.12) follows by (4.3), (4.4), and (3.19).

Proof of Corollary 2.2.

Proof of Theorem 2.3.

Hence, (3.19) and above equation make Open image in new window . Equation (2.14) follows by (3.15).

## Notes

### Acknowledgments

This work is supported by the National NSF (10871226) of China. The authors thank the reviewers for giving very valuable suggestions.

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